{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,29]],"date-time":"2026-10-29T17:31:36Z","timestamp":1793295096848,"version":"4.5.0"},"reference-count":318,"publisher":"Springer Science and Business Media LLC","issue":"7-9","license":[{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T00:00:00Z","timestamp":1761609600000},"content-version":"vor","delay-in-days":27,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Saudi Dent. J."],"published-print":{"date-parts":[[2025,10]]},"DOI":"10.1007\/s44445-025-00077-0","type":"journal-article","created":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T12:59:52Z","timestamp":1761656392000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Applications of artificial intelligence in diagnosis and treatment planning of orthodontics: a narrative review"],"prefix":"10.1007","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7659-9342","authenticated-orcid":false,"given":"Sania","family":"Azizi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9566-9502","authenticated-orcid":false,"given":"Sepehr","family":"Hatampoor","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8786-6223","authenticated-orcid":false,"given":"Shabnam","family":"Tahamtan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,10,28]]},"reference":[{"issue":"3","key":"77_CR1","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1093\/dmfr\/twae005","volume":"53","author":"A Abdulkreem","year":"2024","unstructured":"Abdulkreem A, Bhattacharjee T, Alzaabi H, Alali K, Gonzalez A, Chaudhry J, Prasad S (2024) Artificial intelligence-based automated preprocessing and classification of impacted maxillary canines in panoramic radiographs. Dentomaxillofac Radiol 53(3):173\u2013177. https:\/\/doi.org\/10.1093\/dmfr\/twae005","journal-title":"Dentomaxillofac Radiol"},{"key":"77_CR2","doi-asserted-by":"crossref","unstructured":"Abe JM, Ortega NRS, M\u00e1rio MC, Del Santo M (2005) Paraconsistent artificial neural network:: an application in cephalometric analysis. In: Khosla R, Howlett RJ, Jain LC (eds) KES J, Pt 2, Proceedings Vol 3682, pp 716\u2013723","DOI":"10.1007\/11552451_98"},{"issue":"3","key":"77_CR3","doi-asserted-by":"publisher","first-page":"263","DOI":"10.2319\/071123-484.1","volume":"94","author":"S Abu Arqub","year":"2024","unstructured":"Abu Arqub S, Al-Moghrabi D, Allareddy V, Upadhyay M, Vaid N, Yadav S (2024) Content analysis of AI-generated (ChatGPT) responses concerning orthodontic clear aligners. Angle Orthod 94(3):263\u2013272. https:\/\/doi.org\/10.2319\/071123-484.1","journal-title":"Angle Orthod"},{"issue":"1","key":"77_CR4","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-69314-6","volume":"14","author":"SM Adel","year":"2024","unstructured":"Adel SM, Bichu YM, Pandian SM, Sabouni W, Shah C, Vaiid N (2024) Clinical audit of an artificial intelligence (AI) empowered smile simulation system: a prospective clinical trial. Sci Rep 14(1):19385. https:\/\/doi.org\/10.1038\/s41598-024-69314-6","journal-title":"Sci Rep"},{"issue":"4","key":"77_CR5","doi-asserted-by":"publisher","first-page":"402","DOI":"10.4103\/ijdr.ijdr_955_21","volume":"33","author":"S Agarwal","year":"2022","unstructured":"Agarwal S, Agarwal S (2022) Bone age assessment from lateral cephalograms using deep learning algorithms in the Indian population. Indian J Dent Res 33(4):402\u2013407. https:\/\/doi.org\/10.4103\/ijdr.ijdr_955_21","journal-title":"Indian J Dent Res"},{"key":"77_CR6","doi-asserted-by":"publisher","first-page":"200","DOI":"10.1016\/j.ijom.2023.10.555","volume":"52","author":"H Ahn","year":"2024","unstructured":"Ahn H, Yang B (2024) A comparative study of measurements using manual and artificial intelligence for anatomical landmarks in three-dimensional cone-beam computed tomography images. Eur J Orthod 52:200. https:\/\/doi.org\/10.1016\/j.ijom.2023.10.555","journal-title":"Eur J Orthod"},{"key":"77_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2022.107123","volume":"226","author":"J Ahn","year":"2022","unstructured":"Ahn J, Nguyen TP, Kim YJ, Kim T, Yoon J (2022) Automated analysis of three-dimensional CBCT images taken in natural head position that combines facial profile processing and multiple deep-learning models. Comput Methods Programs Biomed 226:107123. https:\/\/doi.org\/10.1016\/j.cmpb.2022.107123","journal-title":"Comput Methods Programs Biomed"},{"key":"77_CR8","doi-asserted-by":"publisher","DOI":"10.3390\/bioengineering11040318","author":"HJ Ahn","year":"2024","unstructured":"Ahn HJ, Byun SH, Baek SH, Park SY, Yi SM, Park IY, On S-W, Kim J-C, Yang BE (2024) A comparative analysis of artificial intelligence and manual methods for three-dimensional anatomical landmark identification in dentofacial treatment planning. Bioengineering. https:\/\/doi.org\/10.3390\/bioengineering11040318","journal-title":"Bioengineering"},{"issue":"4","key":"77_CR9","doi-asserted-by":"publisher","first-page":"629","DOI":"10.1007\/s11282-023-00678-7","volume":"39","author":"G Akay","year":"2023","unstructured":"Akay G, Akcayol MA, \u00d6zdem K, G\u00fcng\u00f6r K (2023) Deep convolutional neural network-the evaluation of cervical vertebrae maturation. Oral Radiol 39(4):629\u2013638. https:\/\/doi.org\/10.1007\/s11282-023-00678-7","journal-title":"Oral Radiol"},{"issue":"1","key":"77_CR10","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1093\/ejo\/24.1.99","volume":"24","author":"MO Ak\u00e7am","year":"2002","unstructured":"Ak\u00e7am MO, Takada K (2002) Fuzzy modelling for selecting headgear types. Eur J Orthod 24(1):99\u2013106. https:\/\/doi.org\/10.1093\/ejo\/24.1.99","journal-title":"Eur J Orthod"},{"key":"77_CR11","doi-asserted-by":"publisher","DOI":"10.3390\/ijerph17228447","author":"DM Alalharith","year":"2020","unstructured":"Alalharith DM, Alharthi HM, Alghamdi WM, Alsenbel YM, Aslam N, Khan IU, Shahin SY, Diani\u0161kov\u00e1 S, Alhareky MS, Barouch KK (2020) A deep learning-based approach for the detection of early signs of gingivitis in orthodontic patients using faster region-based convolutional neural networks. Int J Environ Res Public Health. https:\/\/doi.org\/10.3390\/ijerph17228447","journal-title":"Int J Environ Res Public Health"},{"key":"77_CR12","doi-asserted-by":"publisher","DOI":"10.3389\/fcell.2020.00789","author":"MK Alam","year":"2020","unstructured":"Alam MK, Alfawzan AA (2020a) Dental characteristics of different types of cleft and non-cleft individuals. Front Cell Dev Biol. https:\/\/doi.org\/10.3389\/fcell.2020.00789","journal-title":"Front Cell Dev Biol"},{"key":"77_CR13","doi-asserted-by":"publisher","DOI":"10.3389\/fcell.2020.00656","author":"MK Alam","year":"2020","unstructured":"Alam MK, Alfawzan AA (2020b) Evaluation of Sella turcica bridging and morphology in different types of cleft patients. Front Cell Dev Biol. https:\/\/doi.org\/10.3389\/fcell.2020.00656","journal-title":"Front Cell Dev Biol"},{"key":"77_CR14","doi-asserted-by":"publisher","DOI":"10.3389\/fped.2021.651951","author":"MK Alam","year":"2021","unstructured":"Alam MK, Alfawzan AA, Haque S, Mok PL, Marya A, Venugopal A, Jamayet NB, Siddiqui AA (2021) Sagittal jaw relationship of different types of cleft and non-cleft individuals. Front Pediatr. https:\/\/doi.org\/10.3389\/fped.2021.651951","journal-title":"Front Pediatr"},{"issue":"Suppl 1","key":"77_CR15","doi-asserted-by":"publisher","first-page":"S540","DOI":"10.4103\/jpbs.jpbs_852_23","volume":"16","author":"MK Alam","year":"2024","unstructured":"Alam MK, Alanazi DSA, Alruwaili SRF, Alderaan RAI (2024) Assessment of AI models in predicting treatment outcomes in orthodontics. J Pharm Bioallied Sci 16(Suppl 1):S540-s542. https:\/\/doi.org\/10.4103\/jpbs.jpbs_852_23","journal-title":"J Pharm Bioallied Sci"},{"issue":"15","key":"77_CR16","first-page":"98","volume":"28","author":"G Aldabbagh","year":"2019","unstructured":"Aldabbagh G, Omar M, Tayeb H, Alafeef R, Aldabbagh R (2019) Simplyortho: a software for orthodontics that automates bracket placement. Int J Adv Sci Technol 28(15):98\u2013112","journal-title":"Int J Adv Sci Technol"},{"issue":"1","key":"77_CR17","doi-asserted-by":"publisher","first-page":"44","DOI":"10.3390\/biomedinformatics3010003","volume":"3","author":"A Alessandri-Bonetti","year":"2023","unstructured":"Alessandri-Bonetti A, Sangalli L, Salerno M, Gallenzi P (2023) Reliability of artificial intelligence-assisted cephalometric analysis. A pilot study. BioMedInformatics 3(1):44\u201353. https:\/\/doi.org\/10.3390\/biomedinformatics3010003","journal-title":"BioMedInformatics"},{"key":"77_CR18","first-page":"74","volume":"28","author":"SM Ali","year":"2021","unstructured":"Ali SM, Saloom HF, Tawfeeq MA (2021) Artificial neural network for prediction of unerupted premolars and canines. Int Med 28:74\u201379","journal-title":"Int Med"},{"issue":"2","key":"77_CR19","doi-asserted-by":"publisher","first-page":"101","DOI":"10.5152\/TurkJOrthod.2022.21087","volume":"35","author":"SM Ali","year":"2022","unstructured":"Ali SM, Saloom HF, Tawfeeq MA (2022) Cephalometric variables prediction from lateral photographs between different skeletal patterns using regression artificial neural networks. Turk J Orthod 35(2):101\u2013111. https:\/\/doi.org\/10.5152\/TurkJOrthod.2022.21087","journal-title":"Turk J Orthod"},{"key":"77_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.imu.2022.100918","author":"M Aljabri","year":"2022","unstructured":"Aljabri M, Aljameel SS, Min-Allah N, Alhuthayfi J, Alghamdi L, Alduhailan N, Alfehaid R, Alqarawi R, Alhareky M, Shahin SY, Al Turki W (2022) Canine impaction classification from panoramic dental radiographic images using deep learning models. Inform Med Unlocked. https:\/\/doi.org\/10.1016\/j.imu.2022.100918","journal-title":"Inform Med Unlocked"},{"issue":"2","key":"77_CR21","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1093\/ejo\/cjac047","volume":"45","author":"KA Alqahtani","year":"2023","unstructured":"Alqahtani KA, Jacobs R, Smolders A, Van Gerven A, Willems H, Shujaat S, Shaheen E (2023) Deep convolutional neural network-based automated segmentation and classification of teeth with orthodontic brackets on cone-beam computed-tomographic images: a validation study. Eur J Orthod 45(2):169\u2013174. https:\/\/doi.org\/10.1093\/ejo\/cjac047","journal-title":"Eur J Orthod"},{"key":"77_CR22","doi-asserted-by":"crossref","unstructured":"Alsubhi R, Alsharif H, Kadi H, Barashi M (2024) Dental crowding prediction from occlusal view images using deep learning. Paper presented at the 2024 IEEE International Conference on Automatic Control and Intelligent Systems, I2CACIS 2024 - Proceedings","DOI":"10.1109\/I2CACIS61270.2024.10649861"},{"key":"77_CR23","doi-asserted-by":"publisher","DOI":"10.1155\/2023\/5933003","volume":"2023","author":"AS Al-Ubaydi","year":"2023","unstructured":"Al-Ubaydi AS, Al-Groosh D (2023) The validity and reliability of automatic tooth segmentation generated using artificial intelligence. Sci World J 2023:5933003. https:\/\/doi.org\/10.1155\/2023\/5933003","journal-title":"Sci World J"},{"issue":"6","key":"77_CR24","doi-asserted-by":"publisher","first-page":"e173","DOI":"10.1016\/j.ajodo.2020.08.014","volume":"158","author":"H Amasya","year":"2020","unstructured":"Amasya H, Cesur E, Y\u0131ld\u0131r\u0131m D, Orhan K (2020a) Validation of cervical vertebral maturation stages: artificial intelligence vs human observer visual analysis. Am J Orthod Dentofacial Orthop 158(6):e173\u2013e179. https:\/\/doi.org\/10.1016\/j.ajodo.2020.08.014","journal-title":"Am J Orthod Dentofacial Orthop"},{"key":"77_CR25","doi-asserted-by":"publisher","DOI":"10.1259\/dmfr.20190441","author":"H Amasya","year":"2020","unstructured":"Amasya H, Yildirim D, Aydogan T, Kemaloglu N, Orhan K (2020b) Cervical vertebral maturation assessment on lateral cephalometric radiographs using artificial intelligence: comparison of machine learning classifier models. Dentomaxillofac Radiol. https:\/\/doi.org\/10.1259\/dmfr.20190441","journal-title":"Dentomaxillofac Radiol"},{"issue":"33","key":"77_CR26","first-page":"402","volume":"2022","author":"N Ameli","year":"2023","unstructured":"Ameli N, Lagravere M, Lai H (2023) Application of deep learning to classify skeletal growth phase on 3D radiographs. Indian J Dent Res 2022(33):402\u2013407","journal-title":"Indian J Dent Res"},{"issue":"1","key":"77_CR27","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1038\/sj.ebd.6401291","volume":"19","author":"N Amintavakoli","year":"2018","unstructured":"Amintavakoli N, Spivakovsky S (2018) Cone-beam computed tomography or conventional radiography for localising of maxillary impacted canines? Evid Based Dent 19(1):22\u201323. https:\/\/doi.org\/10.1038\/sj.ebd.6401291","journal-title":"Evid Based Dent"},{"issue":"5","key":"77_CR28","doi-asserted-by":"publisher","first-page":"571","DOI":"10.1111\/jcpe.13774","volume":"50","author":"KM Andrade","year":"2023","unstructured":"Andrade KM, Silva BPM, de Oliveira LR, Cury PR (2023) Automatic dental biofilm detection based on deep learning. J Clin Periodontol 50(5):571\u2013581. https:\/\/doi.org\/10.1111\/jcpe.13774","journal-title":"J Clin Periodontol"},{"key":"77_CR29","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics14242849","author":"C Arslan","year":"2024","unstructured":"Arslan C, Yucel NO, Kahya K, Sunal Akturk E, Germec Cakan D (2024) Artificial intelligence for tooth detection in cleft lip and palate patients. Diagnostics. https:\/\/doi.org\/10.3390\/diagnostics14242849","journal-title":"Diagnostics"},{"issue":"7","key":"77_CR30","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0269198","volume":"17","author":"SF Atici","year":"2022","unstructured":"Atici SF, Ansari R, Allareddy V, Suhaym O, Cetin AE, Elnagar MH (2022) Fully automated determination of the cervical vertebrae maturation stages using deep learning with directional filters. PLoS ONE 17(7):e0269198. https:\/\/doi.org\/10.1371\/journal.pone.0269198","journal-title":"PLoS ONE"},{"issue":"S1","key":"77_CR31","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1111\/ocr.12644","volume":"26","author":"SF Atici","year":"2023","unstructured":"Atici SF, Ansari R, Allareddy V, Suhaym O, Cetin AE, Elnagar MH (2023) Aggregatenet: a deep learning model for automated classification of cervical vertebrae maturation stages. Orthod Craniofac Res 26(S1):111\u2013117. https:\/\/doi.org\/10.1111\/ocr.12644","journal-title":"Orthod Craniofac Res"},{"issue":"3","key":"77_CR32","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1093\/ejo\/cju038","volume":"37","author":"P Auconi","year":"2015","unstructured":"Auconi P, Scazzocchio M, Cozza P, McNamara JA Jr., Franchi L (2015) Prediction of class III treatment outcomes through orthodontic data mining. Eur J Orthod 37(3):257\u2013267. https:\/\/doi.org\/10.1093\/ejo\/cju038","journal-title":"Eur J Orthod"},{"issue":"1","key":"77_CR33","doi-asserted-by":"publisher","DOI":"10.1186\/s12903-024-05032-9","volume":"24","author":"N Baig","year":"2024","unstructured":"Baig N, Gyasudeen KS, Bhattacharjee T, Chaudhry J, Prasad S (2024) Comparative evaluation of commercially available AI-based cephalometric tracing programs. BMC Oral Health 24(1):1241. https:\/\/doi.org\/10.1186\/s12903-024-05032-9","journal-title":"BMC Oral Health"},{"issue":"6","key":"77_CR34","doi-asserted-by":"publisher","first-page":"622","DOI":"10.1093\/ejo\/cjaa069","volume":"43","author":"S Baksi","year":"2021","unstructured":"Baksi S, Freezer S, Matsumoto T, Dreyer C (2021) Accuracy of an automated method of 3D soft tissue landmark detection. Eur J Orthod 43(6):622\u2013630. https:\/\/doi.org\/10.1093\/ejo\/cjaa069","journal-title":"Eur J Orthod"},{"issue":"1","key":"77_CR35","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1007\/s10916-009-9347-9","volume":"35","author":"A Banumathi","year":"2011","unstructured":"Banumathi A, Raju S, Abhaikumar V (2011) Diagnosis of dental deformities in cephalometry images using support vector machine. J Med Syst 35(1):113\u2013119. https:\/\/doi.org\/10.1007\/s10916-009-9347-9","journal-title":"J Med Syst"},{"issue":"1","key":"77_CR36","doi-asserted-by":"publisher","DOI":"10.1186\/s12903-023-02881-8","volume":"23","author":"H Bao","year":"2023","unstructured":"Bao H, Zhang K, Yu C, Li H, Cao D, Shu H, Liu L, Yan B (2023) Evaluating the accuracy of automated cephalometric analysis based on artificial intelligence. BMC Oral Health 23(1):191. https:\/\/doi.org\/10.1186\/s12903-023-02881-8","journal-title":"BMC Oral Health"},{"issue":"2","key":"77_CR37","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1016\/j.ajodo.2024.03.012","volume":"166","author":"E Bardideh","year":"2024","unstructured":"Bardideh E, Lal Alizadeh F, Amiri M, Ghorbani M (2024) Designing an artificial intelligence system for dental occlusion classification using intraoral photographs: a comparative analysis between artificial intelligence-based and clinical diagnoses. Am J Orthod Dentofacial Orthop 166(2):125\u2013137. https:\/\/doi.org\/10.1016\/j.ajodo.2024.03.012","journal-title":"Am J Orthod Dentofacial Orthop"},{"issue":"6","key":"77_CR38","doi-asserted-by":"publisher","first-page":"e327","DOI":"10.1016\/j.jcms.2014.01.028","volume":"42","author":"P Berssenbr\u00fcgge","year":"2014","unstructured":"Berssenbr\u00fcgge P, Berlin NF, Kebeck G, Runte C, Jung S, Kleinheinz J, Dirksen D (2014) 2D and 3D analysis methods of facial asymmetry in comparison. J Craniomaxillofac Surg 42(6):e327-334. https:\/\/doi.org\/10.1016\/j.jcms.2014.01.028","journal-title":"J Craniomaxillofac Surg"},{"issue":"5","key":"77_CR39","doi-asserted-by":"publisher","first-page":"2255","DOI":"10.1007\/s00784-023-04978-4","volume":"27","author":"FMS Blum","year":"2023","unstructured":"Blum FMS, M\u00f6hlhenrich SC, Raith S, Pankert T, Peters F, Wolf M, H\u00f6lzle F, Modabber A (2023) Evaluation of an artificial intelligence-based algorithm for automated localization of craniofacial landmarks. Clin Oral Investig 27(5):2255\u20132265. https:\/\/doi.org\/10.1007\/s00784-023-04978-4","journal-title":"Clin Oral Investig"},{"issue":"1","key":"77_CR40","doi-asserted-by":"publisher","DOI":"10.1186\/s40510-024-00539-x","volume":"25","author":"S Bor","year":"2024","unstructured":"Bor S, Ci\u011ferim S, Kotan S (2024) Comparison of AI-assisted cephalometric analysis and orthodontist-performed digital tracing analysis. Prog Orthod 25(1):41. https:\/\/doi.org\/10.1186\/s40510-024-00539-x","journal-title":"Prog Orthod"},{"issue":"Suppl 2","key":"77_CR41","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1111\/ocr.12542","volume":"24","author":"G Bulatova","year":"2021","unstructured":"Bulatova G, Kusnoto B, Grace V, Tsay TP, Avenetti DM, Sanchez FJC (2021) Assessment of automatic cephalometric landmark identification using artificial intelligence. Orthod Craniofac Res 24(Suppl 2):37\u201342. https:\/\/doi.org\/10.1111\/ocr.12542","journal-title":"Orthod Craniofac Res"},{"issue":"6","key":"77_CR42","doi-asserted-by":"publisher","first-page":"830","DOI":"10.1016\/j.sdentj.2024.03.008","volume":"36","author":"B Butul","year":"2024","unstructured":"Butul B, Sharab L (2024) Obstacles behind the innovation- a peek into Artificial intelligence in the field of orthodontics \u2013 a literature review. Saudi Dent J 36(6):830\u2013834. https:\/\/doi.org\/10.1016\/j.sdentj.2024.03.008","journal-title":"Saudi Dent J"},{"key":"77_CR43","doi-asserted-by":"publisher","DOI":"10.1016\/j.jdent.2023.104701","volume":"138","author":"J Cai","year":"2023","unstructured":"Cai J, Deng Y, Min Z, Zhang Y, Zhao Z, Jing D (2023) Revealing the representative facial traits of different sagittal skeletal types: decipher what artificial intelligence can see by Grad-CAM. J Dent 138:104701. https:\/\/doi.org\/10.1016\/j.jdent.2023.104701","journal-title":"J Dent"},{"issue":"1","key":"77_CR44","doi-asserted-by":"publisher","DOI":"10.1186\/s12903-023-03817-y","volume":"24","author":"J Cai","year":"2024","unstructured":"Cai J, Min Z, Deng Y, Jing D, Zhao Z (2024) Assessing the impact of occlusal plane rotation on facial aesthetics in orthodontic treatment: a machine learning approach. BMC Oral Health 24(1):30. https:\/\/doi.org\/10.1186\/s12903-023-03817-y","journal-title":"BMC Oral Health"},{"key":"77_CR45","doi-asserted-by":"publisher","unstructured":"Cejudo Grano de Oro JE, Koch PJ, Krois J, Garcia Cantu Ros A, Patel J, Meyer-Lueckel H, Schwendicke F (2022) Hyperparameter tuning and automatic image augmentation for deep learning-based angle classification on intraoral photographs-a retrospective study. Diagnostics (Basel) 12(7). https:\/\/doi.org\/10.3390\/diagnostics12071526","DOI":"10.3390\/diagnostics12071526"},{"issue":"4","key":"77_CR46","doi-asserted-by":"publisher","DOI":"10.1259\/dmfr.20140270","volume":"44","author":"GO Cericato","year":"2015","unstructured":"Cericato GO, Bittencourt MA, Paranhos LR (2015) Validity of the assessment method of skeletal maturation by cervical vertebrae: a systematic review and meta-analysis. Dentomaxillofac Radiol 44(4):20140270. https:\/\/doi.org\/10.1259\/dmfr.20140270","journal-title":"Dentomaxillofac Radiol"},{"issue":"6","key":"77_CR47","doi-asserted-by":"publisher","first-page":"547","DOI":"10.3760\/cma.j.cn112144-20230305-00072","volume":"58","author":"S Chang","year":"2023","unstructured":"Chang S, Wang SF, Zuo FF, Wang F, Gong BW, Wang YJ, Xie XJ (2023) Automated diagnostic classification with lateral cephalograms based on deep learning network model. Zhonghua Kou Qiang Yi Xue Za Zhi 58(6):547\u2013553. https:\/\/doi.org\/10.3760\/cma.j.cn112144-20230305-00072","journal-title":"Zhonghua Kou Qiang Yi Xue Za Zhi"},{"issue":"6","key":"77_CR48","doi-asserted-by":"publisher","first-page":"893","DOI":"10.1111\/ocr.12830","volume":"27","author":"Q Chang","year":"2024","unstructured":"Chang Q, Bai Y, Wang S, Wang F, Wang Y, Zuo F, Xie X (2024) Automatic soft-tissue analysis on orthodontic frontal and lateral facial photographs based on deep learning. Orthod Craniofac Res 27(6):893\u2013902. https:\/\/doi.org\/10.1111\/ocr.12830","journal-title":"Orthod Craniofac Res"},{"issue":"1","key":"77_CR49","doi-asserted-by":"publisher","DOI":"10.1186\/s12938-025-01345-0","volume":"24","author":"Q Chang","year":"2025","unstructured":"Chang Q, Bai Y, Wang S, Wang F, Liang S, Xie X (2025) Automated orthodontic diagnosis via self-supervised learning and multi-attribute classification using lateral cephalograms. Biomed Eng Online 24(1):9. https:\/\/doi.org\/10.1186\/s12938-025-01345-0","journal-title":"Biomed Eng Online"},{"issue":"1","key":"77_CR50","doi-asserted-by":"publisher","first-page":"77","DOI":"10.2319\/012919-59.1","volume":"90","author":"S Chen","year":"2020","unstructured":"Chen S, Wang L, Li G, Wu TH, Diachina S, Tejera B, Kwon JJ, Lin F-C, Lee Y-T, Xu T, Shen D, Ko CC (2020) Machine learning in orthodontics: introducing a 3D auto-segmentation and auto-landmark finder of CBCT images to assess maxillary constriction in unilateral impacted canine patients. Angle Orthod 90(1):77\u201384. https:\/\/doi.org\/10.2319\/012919-59.1","journal-title":"Angle Orthod"},{"key":"77_CR51","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/embc40787.2023.10341102","volume":"2023","author":"J Chen","year":"2023","unstructured":"Chen J, Che H, Sun J, Rao Y, Wu J (2023a) An automatic cephalometric landmark detection method based on heatmap regression and Monte Carlo dropout. Annu Int Conf IEEE Eng Med Biol Soc 2023:1\u20134. https:\/\/doi.org\/10.1109\/embc40787.2023.10341102","journal-title":"Annu Int Conf IEEE Eng Med Biol Soc"},{"key":"77_CR52","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/acf026","author":"Z Chen","year":"2023","unstructured":"Chen Z, Chen S, Hu F (2023b) CTA-UNet: CNN-transformer architecture UNet for dental CBCT images segmentation. Phys Med Biol. https:\/\/doi.org\/10.1088\/1361-6560\/acf026","journal-title":"Phys Med Biol"},{"key":"77_CR53","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2024.109025","volume":"180","author":"H Chen","year":"2024","unstructured":"Chen H, Qu Z, Tian Y, Jiang N, Qin Y, Gao J, Zhang R, Ma Y, Jin Z, Zhai G (2024) A cross-temporal multimodal fusion system based on deep learning for orthodontic monitoring. Comput Biol Med 180:109025. https:\/\/doi.org\/10.1016\/j.compbiomed.2024.109025","journal-title":"Comput Biol Med"},{"issue":"4","key":"77_CR54","first-page":"341","volume":"34","author":"YW Cheong","year":"2011","unstructured":"Cheong YW, Lo LJ (2011) Facial asymmetry: etiology, evaluation, and management. Chang Gung Med J 34(4):341\u2013351","journal-title":"Chang Gung Med J"},{"issue":"5","key":"77_CR55","doi-asserted-by":"publisher","first-page":"557","DOI":"10.2319\/111823-767.1","volume":"94","author":"SJ Cho","year":"2024","unstructured":"Cho SJ, Moon JH, Ko DY, Lee JM, Park JA, Donatelli RE, Lee SJ (2024) Orthodontic treatment outcome predictive performance differences between artificial intelligence and conventional methods. Angle Orthod 94(5):557\u2013565. https:\/\/doi.org\/10.2319\/111823-767.1","journal-title":"Angle Orthod"},{"issue":"1","key":"77_CR56","doi-asserted-by":"publisher","first-page":"1","DOI":"10.7181\/acfs.2015.16.1.1","volume":"16","author":"KY Choi","year":"2015","unstructured":"Choi KY (2015) Analysis of facial asymmetry. Arch Craniofac Surg 16(1):1\u201310. https:\/\/doi.org\/10.7181\/acfs.2015.16.1.1","journal-title":"Arch Craniofac Surg"},{"issue":"11","key":"77_CR57","doi-asserted-by":"publisher","DOI":"10.7759\/cureus.72948","volume":"16","author":"A Chuchra","year":"2024","unstructured":"Chuchra A, Gupta K, Arora R, Bindra S, Hingad N, Babbar A (2024) Digital cephalometric analysis: unveiling the role and reliability of semi-automated OneCeph, artificial intelligence-powered WebCeph mobile app, and semi-automated computer-aided NemoCeph software in orthodontic practice. Cureus 16(11):e72948. https:\/\/doi.org\/10.7759\/cureus.72948","journal-title":"Cureus"},{"key":"77_CR58","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.103720","volume":"120","author":"M Chung","year":"2020","unstructured":"Chung M, Lee M, Hong J, Park S, Lee J, Lee J, Yang I-H, Shin YG (2020) Pose-aware instance segmentation framework from cone beam CT images for tooth segmentation. Comput Biol Med 120:103720. https:\/\/doi.org\/10.1016\/j.compbiomed.2020.103720","journal-title":"Comput Biol Med"},{"issue":"1","key":"77_CR59","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-25215-0","volume":"12","author":"EJ Chung","year":"2022","unstructured":"Chung EJ, Yang BE, Park IY, Yi S, On SW, Kim YH, Kang S-H, Byun SH (2022) Effectiveness of cone-beam computed tomography-generated cephalograms using artificial intelligence cephalometric analysis. Sci Rep 12(1):20585. https:\/\/doi.org\/10.1038\/s41598-022-25215-0","journal-title":"Sci Rep"},{"key":"77_CR60","doi-asserted-by":"publisher","DOI":"10.1590\/2177-6709.27.4.e222112.oar","author":"G \u00c7oban","year":"2022","unstructured":"\u00c7oban G, \u00d6zt\u00fcrk T, Hashimli N, Ya\u011fci A (2022) Comparison between cephalometric measurements using digital manual and web-based artificial intelligence cephalometric tracing software. Dent Press J Orthod. https:\/\/doi.org\/10.1590\/2177-6709.27.4.e222112.oar","journal-title":"Dent Press J Orthod"},{"issue":"9","key":"77_CR61","doi-asserted-by":"publisher","first-page":"3437","DOI":"10.1007\/s00784-019-03019-3","volume":"23","author":"A Coclici","year":"2019","unstructured":"Coclici A, Hede\u015fiu M, Bran S, B\u0103ciu\u0163 M, Dinu C, Rotaru H, Roman R (2019) Early and long-term changes in the muscles of the mandible following orthognathic surgery. Clin Oral Investig 23(9):3437\u20133444. https:\/\/doi.org\/10.1007\/s00784-019-03019-3","journal-title":"Clin Oral Investig"},{"key":"77_CR62","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101949","author":"Z Cui","year":"2021","unstructured":"Cui Z, Li C, Chen N, Wei G, Chen R, Zhou Y, Shen D, Wang W (2021) Tsegnet: an efficient and accurate tooth segmentation network on 3d dental model. Med Image Anal. https:\/\/doi.org\/10.1016\/j.media.2020.101949","journal-title":"Med Image Anal"},{"key":"77_CR63","doi-asserted-by":"publisher","first-page":"132738","DOI":"10.1109\/ACCESS.2019.2940623","volume":"7","author":"X Dai","year":"2019","unstructured":"Dai X, Zhao H, Liu T, Cao D, Xie L (2019) Locating anatomical landmarks on 2D lateral cephalograms through adversarial encoder-decoder networks. IEEE Access 7:132738\u2013132747. https:\/\/doi.org\/10.1109\/ACCESS.2019.2940623","journal-title":"IEEE Access"},{"issue":"4","key":"77_CR64","doi-asserted-by":"publisher","first-page":"194","DOI":"10.1080\/27705781.2023.2254537","volume":"82","author":"H Danisman","year":"2023","unstructured":"Danisman H (2023) Artificial intelligence web-based cephalometric analysis platform: comparison with the computer assisted cephalometric method. Clin Investig Orthod 82(4):194\u2013203. https:\/\/doi.org\/10.1080\/27705781.2023.2254537","journal-title":"Clin Investig Orthod"},{"issue":"3","key":"77_CR65","first-page":"2731","volume":"4","author":"M Dashti","year":"2024","unstructured":"Dashti M, Khosraviani F, Azimi T, Sehat MS, Alekajbaf E, Fahimipour A, Zare N (2024) Predicting mandibular bone growth using artificial intelligence and machine learning: a systematic review. Adv Artif Intell Mach Learn 4(3):2731\u20132745","journal-title":"Adv Artif Intell Mach Learn"},{"key":"77_CR66","doi-asserted-by":"publisher","DOI":"10.3390\/app122412784","author":"M Davidovitch","year":"2022","unstructured":"Davidovitch M, Sella-Tunis T, Abramovicz L, Reiter S, Matalon S, Shpack N (2022) Verification of convolutional neural network cephalometric landmark identification. Appl Sci. https:\/\/doi.org\/10.3390\/app122412784","journal-title":"Appl Sci"},{"key":"77_CR67","doi-asserted-by":"publisher","DOI":"10.1186\/s13005-024-00446-w","author":"CM de Araujo","year":"2024","unstructured":"de Araujo CM, de Jesus Freitas PF, Ferraz AX, Quadras ICC, Zeigelboim BS, Priolo Filho S, K\u00fcchler EC (2024) Sex determination through maxillary dental arch and skeletal base measurements using machine learning. Head Face Med. https:\/\/doi.org\/10.1186\/s13005-024-00446-w","journal-title":"Head Face Med"},{"issue":"1","key":"77_CR68","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1111\/ocr.12863","volume":"28","author":"CM de Araujo","year":"2025","unstructured":"de Araujo CM, Freitas PFdJ, Ferraz AX, Andreis PKDS, Meger MN, Baratto-Filho F, Schroder AGD (2025) Predicting the risk of Maxillary Canine Impaction based on Maxillary measurements using supervised machine learning. Orthod Craniofac Res 28(1):207\u2013215","journal-title":"Orthod Craniofac Res"},{"issue":"3","key":"77_CR69","doi-asserted-by":"publisher","first-page":"1158","DOI":"10.1007\/s10278-022-00766-w","volume":"36","author":"G de Queiroz Tavares Borges Mesquita","year":"2023","unstructured":"de Queiroz Tavares Borges Mesquita G, Vieira WA, Vidigal MTC, Traven\u00e7olo BAN, Beaini TL, Spin-Neto R, Paranhos LR, de Brito J\u00fanior RB (2023) Artificial intelligence for detecting cephalometric landmarks: a systematic review and meta-analysis. J Digit Imaging 36(3):1158\u20131179. https:\/\/doi.org\/10.1007\/s10278-022-00766-w","journal-title":"J Digit Imaging"},{"issue":"115","key":"77_CR70","first-page":"916","volume":"11","author":"S Deepa","year":"2024","unstructured":"Deepa S, Umamageswari A, Sherinbeevi L, Sangari A (2024) Unleashing hidden canines: a novel fast R-CNN based technique for automatic auxiliary canine impaction. Int J Adv Technol Eng Explor 11(115):916","journal-title":"Int J Adv Technol Eng Explor"},{"key":"77_CR71","doi-asserted-by":"crossref","unstructured":"Demircan GS, K\u0131l\u0131\u00e7 B, \u00d6nal-S\u00fczek T (2021) Early Diagnosis and prediction of skeletal class III malocclusion from profile photos using artificial intelligence. Paper presented at the IFMBE Proceedings","DOI":"10.1007\/978-3-030-64610-3_50"},{"issue":"7","key":"77_CR72","doi-asserted-by":"publisher","first-page":"793","DOI":"10.1016\/j.ijom.2022.10.010","volume":"52","author":"HH Deng","year":"2023","unstructured":"Deng HH, Liu Q, Chen A, Kuang T, Yuan P, Gateno J, Kim D, Barber JC, Xiong KG, Yu P, Gu KJ, Xu X, Yan P, Shen D, Xia JJ (2023) Clinical feasibility of deep learning-based automatic head CBCT image segmentation and landmark detection in computer-aided surgical simulation for orthognathic surgery. Int J Oral Maxillofac Surg 52(7):793\u2013800. https:\/\/doi.org\/10.1016\/j.ijom.2022.10.010","journal-title":"Int J Oral Maxillofac Surg"},{"issue":"11","key":"77_CR73","doi-asserted-by":"publisher","first-page":"1380","DOI":"10.1177\/00220345221112333","volume":"101","author":"G Dot","year":"2022","unstructured":"Dot G, Schouman T, Chang S, Rafflenbeul F, Kerbrat A, Rouch P, Gajny L (2022) Automatic 3-dimensional cephalometric landmarking via deep learning. J Dent Res 101(11):1380\u20131387. https:\/\/doi.org\/10.1177\/00220345221112333","journal-title":"J Dent Res"},{"key":"77_CR74","doi-asserted-by":"publisher","unstructured":"Duman \u015eB, Syed AZ, Celik Ozen D, Bayrakdar \u0130, Salehi HS, Abdelkarim A, . . . Orhan K (2022) Convolutional neural network performance for sella turcica segmentation and classification using CBCT Images. Diagnostics (Basel) 12(9). https:\/\/doi.org\/10.3390\/diagnostics12092244","DOI":"10.3390\/diagnostics12092244"},{"issue":"3","key":"77_CR75","doi-asserted-by":"publisher","first-page":"481","DOI":"10.1111\/ocr.12633","volume":"26","author":"GS Duran","year":"2023","unstructured":"Duran GS, G\u00f6kmen \u015e, Topsakal KG, G\u00f6rg\u00fcl\u00fc S (2023) Evaluation of the accuracy of fully automatic cephalometric analysis software with artificial intelligence algorithm. Orthod Craniofac Res 26(3):481\u2013490. https:\/\/doi.org\/10.1111\/ocr.12633","journal-title":"Orthod Craniofac Res"},{"issue":"1","key":"77_CR76","doi-asserted-by":"publisher","DOI":"10.1186\/s12911-024-02619-8","volume":"24","author":"D Dursun","year":"2024","unstructured":"Dursun D, Bilici Ge\u00e7er R (2024) Can artificial intelligence models serve as patient information consultants in orthodontics? BMC Med Inform Decis Mak 24(1):211. https:\/\/doi.org\/10.1186\/s12911-024-02619-8","journal-title":"BMC Med Inform Decis Mak"},{"key":"77_CR77","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics12092176","author":"AR El Bsat","year":"2022","unstructured":"El Bsat AR, Shammas E, Asmar D, Sakr GE, Zeno KG, Macari AT, Ghafari JG (2022) Semantic segmentation of Maxillary teeth and palatal rugae in two-dimensional images. Diagnostics. https:\/\/doi.org\/10.3390\/diagnostics12092176","journal-title":"Diagnostics"},{"issue":"Suppl 1","key":"77_CR78","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1007\/s00056-023-00459-1","volume":"85","author":"M El-Dawlatly","year":"2024","unstructured":"El-Dawlatly M, Attia KH, Abdelghaffar AY, Mostafa YA, Abd El-Ghafour M (2024) Preciseness of artificial intelligence for lateral cephalometric measurements. J Orofacial Orthopedics \/ Fortschritte der Kieferorthop\u00e4die 85(Suppl 1):27\u201333. https:\/\/doi.org\/10.1007\/s00056-023-00459-1","journal-title":"J Orofacial Orthopedics \/ Fortschritte der Kieferorthop\u00e4die"},{"issue":"6","key":"77_CR79","doi-asserted-by":"publisher","first-page":"608","DOI":"10.1016\/j.ajodo.2024.07.020","volume":"166","author":"T ElShebiny","year":"2024","unstructured":"ElShebiny T, Paradis AE, Kasper FK, Palomo JM (2024) Assessment of virtual bracket removal by artificial intelligence and thermoplastic retainer fit. J Orofac Orthop 166(6):608\u2013615. https:\/\/doi.org\/10.1016\/j.ajodo.2024.07.020","journal-title":"J Orofac Orthop"},{"issue":"4","key":"77_CR80","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1093\/ejo\/10.4.283","volume":"10","author":"S Ericson","year":"1988","unstructured":"Ericson S, Kurol J (1988) Early treatment of palatally erupting maxillary canines by extraction of the primary canines. Eur J Orthod 10(4):283\u2013295. https:\/\/doi.org\/10.1093\/ejo\/10.4.283","journal-title":"Eur J Orthod"},{"key":"77_CR81","doi-asserted-by":"publisher","DOI":"10.3390\/bioengineering11090888","author":"LE Etemad","year":"2024","unstructured":"Etemad LE, Heiner JP, Amin AA, Wu TH, Chao WL, Hsieh SJ, Ko CC (2024) Effectiveness of machine learning in predicting orthodontic tooth extractions: a multi-institutional study. Bioengineering. https:\/\/doi.org\/10.3390\/bioengineering11090888","journal-title":"Bioengineering"},{"key":"77_CR82","doi-asserted-by":"publisher","unstructured":"Etemad L, Wu TH, Heiner P, Liu J, Lee S, Chao WL, . . . Ko CC (2021) Machine learning from clinical data sets of a contemporary decision for orthodontic tooth extraction. Orthod Craniofac Res 24 Suppl 2:193\u2013200. https:\/\/doi.org\/10.1111\/ocr.12502","DOI":"10.1111\/ocr.12502"},{"key":"77_CR83","doi-asserted-by":"publisher","unstructured":"Feng H, Song W, Li R, Yang L, Chen X, Guo J, . . . Wang J (2025) A fully integrated orthodontic aligner with force sensing ability for machine learning-assisted diagnosis. Adv Sci (Weinh) 12(2):e2411187. https:\/\/doi.org\/10.1002\/advs.202411187","DOI":"10.1002\/advs.202411187"},{"issue":"2","key":"77_CR84","doi-asserted-by":"publisher","first-page":"194","DOI":"10.1016\/j.ajodo.2022.11.020","volume":"164","author":"T Ferlito","year":"2023","unstructured":"Ferlito T, Hsiou D, Hargett K, Herzog C, Bachour P, Katebi N, Tokede O, Larson B, Masoud MI (2023) Assessment of artificial intelligence-based remote monitoring of clear aligner therapy: a prospective study. Am J Orthod Dentofacial Orthop 164(2):194\u2013200. https:\/\/doi.org\/10.1016\/j.ajodo.2022.11.020","journal-title":"Am J Orthod Dentofacial Orthop"},{"issue":"Suppl 2","key":"77_CR85","doi-asserted-by":"publisher","first-page":"S1798","DOI":"10.4103\/jpbs.jpbs_1147_23","volume":"16","author":"P Gaonkar","year":"2024","unstructured":"Gaonkar P, Mohammed I, Ribin M, Kumar CD, Thomas PA, Saini R (2024) Assessing the impact of AI-enhanced diagnostic tools on the treatment planning of orthodontic cases: an RCT. J Pharm Bioallied Sci 16(Suppl 2):S1798\u2013S1800. https:\/\/doi.org\/10.4103\/jpbs.jpbs_1147_23","journal-title":"J Pharm Bioallied Sci"},{"issue":"4","key":"77_CR86","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajodo.2021.11.011","volume":"161","author":"SM Gil","year":"2022","unstructured":"Gil SM, Kim I, Cho JH, Hong M, Kim M, Kim SJ, Kang KH (2022) Accuracy of auto-identification of the posteroanterior cephalometric landmarks using cascade convolution neural network algorithm and cephalometric images of different quality from nationwide multiple centers. Am J Orthod Dentofacial Orthop 161(4):e361. https:\/\/doi.org\/10.1016\/j.ajodo.2021.11.011","journal-title":"Am J Orthod Dentofacial Orthop"},{"issue":"1","key":"77_CR87","doi-asserted-by":"publisher","DOI":"10.1186\/s12880-024-01478-z","volume":"24","author":"M Gonca","year":"2024","unstructured":"Gonca M, Bayrakdar \u0130, \u00c7elik \u00d6 (2024a) Does the FARNet neural network algorithm accurately identify posteroanterior cephalometric landmarks? BMC Med Imaging 24(1):294. https:\/\/doi.org\/10.1186\/s12880-024-01478-z","journal-title":"BMC Med Imaging"},{"key":"77_CR88","doi-asserted-by":"publisher","DOI":"10.1007\/s00056-023-00510-1","author":"M Gonca","year":"2024","unstructured":"Gonca M, Sert MF, Gunacar DN, Kose TE, Beser B (2024b) Determination of growth and developmental stages in hand\u2013wrist radiographs: can fractal analysis in combination with artificial intelligence be used? J Orofac Orthop. https:\/\/doi.org\/10.1007\/s00056-023-00510-1","journal-title":"J Orofac Orthop"},{"key":"77_CR89","doi-asserted-by":"publisher","DOI":"10.1016\/j.jdent.2025.105570","volume":"154","author":"B Gong","year":"2025","unstructured":"Gong B, Chang Q, Shi T, Wang S, Wang Y, Zuo F, Xie X, Bai Y (2025) Research of orthodontic soft tissue profile prediction based on conditional generative adversarial networks. J Dent 154:105570. https:\/\/doi.org\/10.1016\/j.jdent.2025.105570","journal-title":"J Dent"},{"key":"77_CR90","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcms.2024.03.023","author":"R Grillo","year":"2024","unstructured":"Grillo R, Quinta Reis BA, Lima BC, Peral Ferreira Pinto LA, Melhem-Elias F (2024) Frontal facial analysis of female celebrity attractiveness standards through artificial intelligence. J Craniomaxillofac Surg. https:\/\/doi.org\/10.1016\/j.jcms.2024.03.023","journal-title":"J Craniomaxillofac Surg"},{"issue":"4","key":"77_CR91","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1111\/jerd.13156","volume":"36","author":"C Guinot-Barona","year":"2024","unstructured":"Guinot-Barona C, Alonso P\u00e9rez-Barquero J, Gal\u00e1n L\u00f3pez L, Barmak AB, Att W, Kois JC, Revilla-Le\u00f3n M (2024) Cephalometric analysis performance discrepancy between orthodontists and an artificial intelligence model using lateral cephalometric radiographs. J Esthet Restor Dent 36(4):555\u2013565. https:\/\/doi.org\/10.1111\/jerd.13156","journal-title":"J Esthet Restor Dent"},{"issue":"1","key":"77_CR92","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1186\/s13005-023-00397-8","volume":"19","author":"RZ Guo","year":"2023","unstructured":"Guo RZ, Tian Y, Li XB, Li WR, He DQ, Sun YN (2023) Facial profile evaluation and prediction of skeletal class II patients during camouflage extraction treatment: a pilot study. Head Face Med 19(1):11. https:\/\/doi.org\/10.1186\/s13005-023-00397-8","journal-title":"Head Face Med"},{"issue":"2","key":"77_CR93","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1111\/ocr.12737","volume":"27","author":"M Gurgel","year":"2024","unstructured":"Gurgel M, Alvarez MA, Aristizabal JF, Baquero B, Gillot M, Al Turkestani N, Cevidanes L (2024) Automated artificial intelligence-based three-dimensional comparison of orthodontic treatment outcomes with and without piezocision surgery. Orthod Craniofac Res 27(2):321\u2013331. https:\/\/doi.org\/10.1111\/ocr.12737","journal-title":"Orthod Craniofac Res"},{"issue":"2","key":"77_CR94","doi-asserted-by":"publisher","first-page":"215","DOI":"10.15386\/mpr-2702","volume":"97","author":"M Hack","year":"2024","unstructured":"Hack M, Dr\u0103gulin B, Hack L, ElSaafin M, Dumitrescu I, Stan D, P\u0103curar M (2024) Comparative study on the results of orthodontic diagnostics by using algorithms generated by artificial intelligence and simple algorithms. Med Pharm Rep 97(2):215\u2013221. https:\/\/doi.org\/10.15386\/mpr-2702","journal-title":"Med Pharm Rep"},{"issue":"1","key":"77_CR95","doi-asserted-by":"publisher","first-page":"48","DOI":"10.4041\/kjod23.075","volume":"54","author":"SH Han","year":"2024","unstructured":"Han SH, Lim J, Kim JS, Cho JH, Hong M, Kim M, Kim S-J, Kim Y-J, Kim YH, Lim S-H, Sung SJ, Kang K-H, Baek S-H, Choi S-K, Kim N (2024) Accuracy of posteroanterior cephalogram landmarks and measurements identification using a cascaded convolutional neural network algorithm: a multicenter study. Korean J Orthod 54(1):48\u201358. https:\/\/doi.org\/10.4041\/kjod23.075","journal-title":"Korean J Orthod"},{"key":"77_CR96","doi-asserted-by":"publisher","unstructured":"Harun MF, Samah AA, Shabuli MIA, Majid HA, Hashim H, Ismail NA, . . . Alias A (2022) Incisor malocclusion using cut-out method and convolutional neural network. Prog Microbe Molecul Biol 5(1). https:\/\/doi.org\/10.36877\/pmmb.a0000279","DOI":"10.36877\/pmmb.a0000279"},{"issue":"3","key":"77_CR97","doi-asserted-by":"publisher","first-page":"394","DOI":"10.4012\/dmj.2023-253","volume":"43","author":"H Hase","year":"2024","unstructured":"Hase H, Mine Y, Okazaki S, Yoshimi Y, Ito S, Peng TY, Sano M, Koizumi Y, Kakimoto N, Tanimoto K, Murayama T (2024) Sex estimation from maxillofacial radiographs using a deep learning approach. Dent Mater J 43(3):394\u2013399. https:\/\/doi.org\/10.4012\/dmj.2023-253","journal-title":"Dent Mater J"},{"key":"77_CR98","doi-asserted-by":"publisher","DOI":"10.1093\/ejo\/cjae029","author":"J Hendrickx","year":"2024","unstructured":"Hendrickx J, Gracea RS, Vanheers M, Winderickx N, Preda F, Shujaat S, Jacobs R (2024) Can artificial intelligence-driven cephalometric analysis replace manual tracing? A systematic review and meta-analysis. Eur J Orthod. https:\/\/doi.org\/10.1093\/ejo\/cjae029","journal-title":"Eur J Orthod"},{"issue":"5","key":"77_CR99","doi-asserted-by":"publisher","first-page":"690","DOI":"10.1016\/j.ajodo.2023.04.019","volume":"164","author":"K Homsi","year":"2023","unstructured":"Homsi K, Snider V, Kusnoto B, Atsawasuwan P, Viana G, Allareddy V, Gajendrareddy P, Elnagar MH (2023) In-vivo evaluation of artificial intelligence driven remote monitoring technology for tracking tooth movement and reconstruction of 3-dimensional digital models during orthodontic treatment. Am J Orthod Dentofacial Orthop 164(5):690\u2013699. https:\/\/doi.org\/10.1016\/j.ajodo.2023.04.019","journal-title":"Am J Orthod Dentofacial Orthop"},{"issue":"4","key":"77_CR100","doi-asserted-by":"publisher","first-page":"287","DOI":"10.4041\/kjod21.248","volume":"52","author":"M Hong","year":"2022","unstructured":"Hong M, Kim I, Cho JH, Kang KH, Kim M, Kim SJ, Kim Y-J, Sung S-J, Kim YH, Lim S-H, Kim N, Baek SH (2022) Accuracy of artificial intelligence-assisted landmark identification in serial lateral cephalograms of Class III patients who underwent orthodontic treatment and two-jaw orthognathic surgery. Korean J Orthod 52(4):287\u2013297. https:\/\/doi.org\/10.4041\/kjod21.248","journal-title":"Korean J Orthod"},{"key":"77_CR101","doi-asserted-by":"publisher","first-page":"23326","DOI":"10.1109\/access.2023.3254206","volume":"11","author":"HQ Hu","year":"2023","unstructured":"Hu HQ, Li ZX, Gao WC (2023) Mpcnet: improved meshsegnet based on position encoding and channel attention. IEEE Access 11:23326\u201323334. https:\/\/doi.org\/10.1109\/access.2023.3254206","journal-title":"IEEE Access"},{"key":"77_CR102","doi-asserted-by":"publisher","DOI":"10.1016\/j.jdent.2024.105398","volume":"151","author":"Y Hu","year":"2024","unstructured":"Hu Y, Liu C, Liu W, Xiong Y, Zeng W, Chen J, Li X, Guo J, Tang W (2024) Fully automated method for three-dimensional segmentation and fine classification of mixed dentition in cone-beam computed tomography using deep learning. J Dent 151:105398. https:\/\/doi.org\/10.1016\/j.jdent.2024.105398","journal-title":"J Dent"},{"issue":"1","key":"77_CR103","doi-asserted-by":"publisher","first-page":"69","DOI":"10.2319\/022019-129.1","volume":"90","author":"HW Hwang","year":"2020","unstructured":"Hwang HW, Park JH, Moon JH, Yu Y, Kim H, Her SB, Lee SJ (2020) Automated identification of cephalometric landmarks: Part 2: might it be better than human? Angle Orthod 90(1):69\u201376. https:\/\/doi.org\/10.2319\/022019-129.1","journal-title":"Angle Orthod"},{"issue":"3","key":"77_CR104","doi-asserted-by":"publisher","first-page":"329","DOI":"10.2319\/021220-100.1","volume":"91","author":"HW Hwang","year":"2021","unstructured":"Hwang HW, Moon JH, Kim MG, Donatelli RE, Lee SJ (2021) Evaluation of automated cephalometric analysis based on the latest deep learning method. Angle Orthod 91(3):329\u2013335. https:\/\/doi.org\/10.2319\/021220-100.1","journal-title":"Angle Orthod"},{"issue":"1","key":"77_CR105","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-13595-2","volume":"12","author":"J Im","year":"2022","unstructured":"Im J, Kim JY, Yu HS, Lee KJ, Choi SH, Kim JH, Cha JY (2022) Accuracy and efficiency of automatic tooth segmentation in digital dental models using deep learning. Sci Rep 12(1):9429. https:\/\/doi.org\/10.1038\/s41598-022-13595-2","journal-title":"Sci Rep"},{"key":"77_CR106","doi-asserted-by":"publisher","DOI":"10.1259\/dmfr.20220362","author":"S Indermun","year":"2023","unstructured":"Indermun S, Shaik S, Nyirenda C, Johannes K, Mulder R (2023) Human examination and artificial intelligence in cephalometric landmark detection-is AI ready to take over? Dentomaxillofac Radiol. https:\/\/doi.org\/10.1259\/dmfr.20220362","journal-title":"Dentomaxillofac Radiol"},{"key":"77_CR107","doi-asserted-by":"publisher","DOI":"10.3390\/jcm13216346","author":"S Ito","year":"2024","unstructured":"Ito S, Mine Y, Urabe S, Yoshimi Y, Okazaki S, Sano M, Koizumi Y, Peng T-Y, Kakimoto N, Murayama T, Tanimoto K (2024) Prediction of a cephalometric parameter and skeletal patterns from lateral profile photographs: a retrospective comparative analysis of regression convolutional neural networks. J Clin Med. https:\/\/doi.org\/10.3390\/jcm13216346","journal-title":"J Clin Med"},{"issue":"1","key":"77_CR108","doi-asserted-by":"publisher","DOI":"10.1186\/s40510-021-00358-4","volume":"22","author":"S Jeon","year":"2021","unstructured":"Jeon S, Lee KC (2021) Comparison of cephalometric measurements between conventional and automatic cephalometric analysis using convolutional neural network. Prog Orthod 22(1):14. https:\/\/doi.org\/10.1186\/s40510-021-00358-4","journal-title":"Prog Orthod"},{"issue":"1","key":"77_CR109","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1097\/SCS.0000000000009862","volume":"35","author":"SM Jeon","year":"2024","unstructured":"Jeon SM, Kim S, Lee KC (2024) Deep learning-based assessment of facial asymmetry using U-net deep convolutional neural network algorithm. J Craniofac Surg 35(1):133\u2013136. https:\/\/doi.org\/10.1097\/SCS.0000000000009862","journal-title":"J Craniofac Surg"},{"key":"77_CR110","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/ac9880","author":"F Jiang","year":"2022","unstructured":"Jiang F, Guo Y, Zhou Y, Yang C, Xing K, Zhou J, Lin Y, Cheng F, Li J (2022a) Automated calibration system for length measurement of lateral cephalometry based on deep learning. Phys Med Biol. https:\/\/doi.org\/10.1088\/1361-6560\/ac9880","journal-title":"Phys Med Biol"},{"key":"77_CR111","doi-asserted-by":"publisher","DOI":"10.3390\/jcm12010055","author":"Y Jiang","year":"2022","unstructured":"Jiang Y, Shang F, Peng J, Liang J, Fan Y, Yang Z, Qi Y, Yang Y, Xu T, Jiang R (2022b) Automatic masseter muscle accurate segmentation from CBCT using deep learning-based model. J Clin Med. https:\/\/doi.org\/10.3390\/jcm12010055","journal-title":"J Clin Med"},{"issue":"1","key":"77_CR112","doi-asserted-by":"publisher","DOI":"10.3390\/jcm12010055","volume":"12","author":"Y Jiang","year":"2022","unstructured":"Jiang Y, Shang F, Peng J, Liang J, Fan Y, Yang Z, Qi Y, Yang Y, Xu T, Jiang R (2022c) Automatic masseter muscle accurate segmentation from CBCT using deep learning-based model. J Clin Med 12(1):55","journal-title":"J Clin Med"},{"issue":"1","key":"77_CR113","doi-asserted-by":"publisher","DOI":"10.1259\/dmfr.20220081","volume":"52","author":"F Jiang","year":"2023","unstructured":"Jiang F, Guo Y, Yang C, Zhou Y, Lin Y, Cheng F, Quan S, Feng Q, Li J (2023) Artificial intelligence system for automated landmark localization and analysis of cephalometry. Dentomaxillofac Radiol 52(1):20220081. https:\/\/doi.org\/10.1259\/dmfr.20220081","journal-title":"Dentomaxillofac Radiol"},{"issue":"1","key":"77_CR114","doi-asserted-by":"publisher","DOI":"10.1007\/s10916-023-01977-6","volume":"47","author":"T Johannes","year":"2023","unstructured":"Johannes T, Akhilanand C, Joachim K, Shankeeth V, Anahita H, Saeed Reza M, Hossein MR (2023) Evaluation of AI model for Cephalometric landmark classification (TG Dental). J Med Syst 47(1):92. https:\/\/doi.org\/10.1007\/s10916-023-01977-6","journal-title":"J Med Syst"},{"issue":"1","key":"77_CR115","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1016\/j.ajodo.2015.07.030","volume":"149","author":"S-K Jung","year":"2016","unstructured":"Jung S-K, Kim T-W (2016) New approach for the diagnosis of extractions with neural network machine learning. Am J Orthod Dentofacial Orthop 149(1):127\u2013133. https:\/\/doi.org\/10.1016\/j.ajodo.2015.07.030","journal-title":"Am J Orthod Dentofacial Orthop"},{"key":"77_CR116","doi-asserted-by":"publisher","unstructured":"Kamei G, Batra P, Singh AK, Arora G, Kaushik S (2024) Development of an artificial intelligence-based algorithm for the assessment of skeletal age and detection of cervical vertebral anomalies in patients with cleft lip and palate. Cleft Palate Craniofac J 10556656241299890. https:\/\/doi.org\/10.1177\/10556656241299890","DOI":"10.1177\/10556656241299890"},{"issue":"1","key":"77_CR117","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1111\/ocr.12683","volume":"27","author":"S Kang","year":"2024","unstructured":"Kang S, Kim I, Kim YJ, Kim N, Baek SH, Sung SJ (2024) Accuracy and clinical validity of automated cephalometric analysis using convolutional neural networks. Orthod Craniofac Res 27(1):64\u201377. https:\/\/doi.org\/10.1111\/ocr.12683","journal-title":"Orthod Craniofac Res"},{"issue":"4","key":"77_CR118","doi-asserted-by":"publisher","first-page":"271","DOI":"10.25259\/APOS_138_2021","volume":"12","author":"D Katyal","year":"2022","unstructured":"Katyal D, Balakrishnan N (2022) Evaluation of the accuracy and reliability of WebCeph \u2013 an artificial intelligence-based online software. APOS Trends Orthod 12(4):271\u2013276. https:\/\/doi.org\/10.25259\/APOS_138_2021","journal-title":"APOS Trends Orthod"},{"key":"77_CR119","doi-asserted-by":"publisher","DOI":"10.3390\/jcm12206621","author":"N Kazimierczak","year":"2023","unstructured":"Kazimierczak N, Kazimierczak W, Serafin Z, Nowicki P, Lemanowicz A, Nadolska K, Janiszewska-Olszowska J (2023) Correlation analysis of nasal septum deviation and results of AI-driven automated 3D cephalometric analysis. J Clin Med. https:\/\/doi.org\/10.3390\/jcm12206621","journal-title":"J Clin Med"},{"issue":"1","key":"77_CR120","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1093\/dmfr\/twad006","volume":"53","author":"N Kazimierczak","year":"2024","unstructured":"Kazimierczak N, Kazimierczak W, Serafin Z, Nowicki P, Jankowski T, Jankowska A, Janiszewska-Olszowska J (2024a) Skeletal facial asymmetry: reliability of manual and artificial intelligence-driven analysis. Dentomaxillofac Radiol 53(1):52\u201359. https:\/\/doi.org\/10.1093\/dmfr\/twad006","journal-title":"Dentomaxillofac Radiol"},{"key":"77_CR121","doi-asserted-by":"publisher","DOI":"10.3390\/jcm13133733","author":"W Kazimierczak","year":"2024","unstructured":"Kazimierczak W, Gawin G, Janiszewska-Olszowska J, Dyszkiewicz-Konwi\u0144ska M, Nowicki P, Kazimierczak N, Serafin Z, Orhan K (2024b) Comparison of three commercially available, AI-driven cephalometric analysis tools in orthodontics. J Clin Med. https:\/\/doi.org\/10.3390\/jcm13133733","journal-title":"J Clin Med"},{"key":"77_CR122","doi-asserted-by":"publisher","DOI":"10.3390\/jcm13144047","author":"W Kazimierczak","year":"2024","unstructured":"Kazimierczak W, Jedli\u0144ski M, Issa J, Kazimierczak N, Janiszewska-Olszowska J, Dyszkiewicz-Konwi\u0144ska M, Orhan K (2024c) Accuracy of artificial intelligence for cervical vertebral maturation assessment-a systematic review. J Clin Med. https:\/\/doi.org\/10.3390\/jcm13144047","journal-title":"J Clin Med"},{"key":"77_CR123","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics14222524","author":"Z Khabadze","year":"2024","unstructured":"Khabadze Z, Mordanov O, Shilyaeva E (2024) Comparative analysis of 3D cephalometry provided with artificial intelligence and manual tracing. Diagnostics. https:\/\/doi.org\/10.3390\/diagnostics14222524","journal-title":"Diagnostics"},{"key":"77_CR124","doi-asserted-by":"crossref","unstructured":"Khan S, Rahmani H, Shah SAA, Bennamoun M, Medioni G, Dickinson S (2018) A guide to convolutional neural networks for computer vision","DOI":"10.1007\/978-3-031-01821-3"},{"issue":"2","key":"77_CR125","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1016\/j.ejwf.2023.02.003","volume":"12","author":"M Khazaei","year":"2023","unstructured":"Khazaei M, Mollabashi V, Khotanlou H, Farhadian M (2023) Automatic determination of pubertal growth spurts based on the cervical vertebral maturation staging using deep convolutional neural networks. J World Fed Orthod 12(2):56\u201363. https:\/\/doi.org\/10.1016\/j.ejwf.2023.02.003","journal-title":"J World Fed Orthod"},{"issue":"1","key":"77_CR126","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajodo.2021.06.012","volume":"161","author":"P Khosravi-Kamrani","year":"2022","unstructured":"Khosravi-Kamrani P, Qiao X, Zanardi G, Wiesen CA, Slade G, Frazier-Bowers SA (2022) A machine learning approach to determine the prognosis of patients with class III malocclusion. Am J Orthod Dentofacial Orthop 161(1):e1. https:\/\/doi.org\/10.1016\/j.ajodo.2021.06.012","journal-title":"Am J Orthod Dentofacial Orthop"},{"issue":"1","key":"77_CR127","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1016\/j.jds.2023.05.001","volume":"19","author":"B K\u0131l\u0131\u00e7","year":"2024","unstructured":"K\u0131l\u0131\u00e7 B, \u0130brahim AH, Aksoy S, Sakman MC, Demircan GS, \u00d6nal-S\u00fczek T (2024) A family-centered orthodontic screening approach using a machine learning-based mobile application. J Dent Sci 19(1):186\u2013195. https:\/\/doi.org\/10.1016\/j.jds.2023.05.001","journal-title":"J Dent Sci"},{"issue":"6","key":"77_CR128","doi-asserted-by":"publisher","first-page":"e906","DOI":"10.1016\/j.jormas.2022.07.017","volume":"123","author":"DD K\u0131l\u0131n\u00e7","year":"2022","unstructured":"K\u0131l\u0131n\u00e7 DD, K\u0131rcelli BH, Sadry S, Karaman A (2022) Evaluation and comparison of smartphone application tracing, web based artificial intelligence tracing and conventional hand tracing methods. J Stomatol Oral Maxillofac Surg 123(6):e906\u2013e915. https:\/\/doi.org\/10.1016\/j.jormas.2022.07.017","journal-title":"J Stomatol Oral Maxillofac Surg"},{"key":"77_CR129","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2020.105513","volume":"194","author":"H Kim","year":"2020","unstructured":"Kim H, Shim E, Park J, Kim YJ, Lee U, Kim Y (2020a) Web-based fully automated cephalometric analysis by deep learning. Comput Methods Programs Biomed 194:105513. https:\/\/doi.org\/10.1016\/j.cmpb.2020.105513","journal-title":"Comput Methods Programs Biomed"},{"issue":"Suppl 2","key":"77_CR130","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1111\/ocr.12514","volume":"24","author":"DW Kim","year":"2021","unstructured":"Kim DW, Kim J, Kim T, Kim T, Kim YJ, Song IS, Kim D-W, Kim Y-J, Song I-S, Ahn B, Choo J, Lee D-Y, Lee DY (2021a) Prediction of hand-wrist maturation stages based on cervical vertebrae images using artificial intelligence. Orthod Craniofac Res 24(Suppl 2):68\u201375. https:\/\/doi.org\/10.1111\/ocr.12514","journal-title":"Orthod Craniofac Res"},{"issue":"S2","key":"77_CR131","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1111\/ocr.12493","volume":"24","author":"J Kim","year":"2021","unstructured":"Kim J, Kim I, Kim YJ, Kim M, Cho JH, Hong M, Kim Y-J, Cho J-H, Kang K-H, Lim S-H, Kim S-J, Kim YH, Kim N, Sung S-J, Baek S-H, Baek SH (2021b) Accuracy of automated identification of lateral cephalometric landmarks using cascade convolutional neural networks on lateral cephalograms from nationwide multi-centres. Orthod Craniofac Res 24(S2):59\u201367. https:\/\/doi.org\/10.1111\/ocr.12493","journal-title":"Orthod Craniofac Res"},{"key":"77_CR132","doi-asserted-by":"publisher","DOI":"10.3390\/s21020505","author":"MJ Kim","year":"2021","unstructured":"Kim MJ, Liu Y, Oh SH, Ahn HW, Kim SH, Nelson G (2021c) Automatic cephalometric landmark identification system based on the multi-stage convolutional neural networks with CBCT combination images. Sensors. https:\/\/doi.org\/10.3390\/s21020505","journal-title":"Sensors"},{"issue":"2","key":"77_CR133","doi-asserted-by":"publisher","first-page":"77","DOI":"10.4041\/kjod.2021.51.2.77","volume":"51","author":"MJ Kim","year":"2021","unstructured":"Kim MJ, Liu Y, Oh SH, Ahn HW, Kim SH, Nelson G (2021d) Evaluation of a multi-stage convolutional neural network-based fully automated landmark identification system using cone-beam computed tomography-synthesized posteroanterior cephalometric images. Korean J Orthod 51(2):77\u201385. https:\/\/doi.org\/10.4041\/kjod.2021.51.2.77","journal-title":"Korean J Orthod"},{"issue":"1","key":"77_CR134","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-15856-6","volume":"12","author":"HJ Kim","year":"2022","unstructured":"Kim HJ, Kim KD, Kim DH (2022) Deep convolutional neural network-based skeletal classification of cephalometric image compared with automated-tracing software. Sci Rep 12(1):11659. https:\/\/doi.org\/10.1038\/s41598-022-15856-6","journal-title":"Sci Rep"},{"key":"77_CR135","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics13213369","author":"E Kim","year":"2023","unstructured":"Kim E, Kuroda Y, Soeda Y, Koizumi S, Yamaguchi T (2023a) Validation of machine learning models for craniofacial growth prediction. Diagnostics. https:\/\/doi.org\/10.3390\/diagnostics13213369","journal-title":"Diagnostics"},{"key":"77_CR136","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-33058-6","author":"H Kim","year":"2023","unstructured":"Kim H, Kim CS, Lee JM, Lee JJ, Lee J, Kim JS, Choi SH (2023b) Prediction of Fishman\u2019s skeletal maturity indicators using artificial intelligence. Sci Rep. https:\/\/doi.org\/10.1038\/s41598-023-33058-6","journal-title":"Sci Rep"},{"key":"77_CR137","doi-asserted-by":"publisher","unstructured":"Kim I, Misra D, Rodriguez L, Gill M, Liberton DK, Almpani K, . . . Antani S (2020) Malocclusion classification on 3D cone-beam CT craniofacial images using multi-channel deep learning models(). Annu Int Conf IEEE Eng Med Biol Soc 2020:1294\u20131298. https:\/\/doi.org\/10.1109\/embc44109.2020.9176672","DOI":"10.1109\/embc44109.2020.9176672"},{"key":"77_CR138","doi-asserted-by":"crossref","unstructured":"King CH, Wang YL, Lin WY, Tsai CL (2022, 28\u201331 March 2022) Automatic cephalometric landmark detection on X-ray images using object detection. Paper presented at the 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)","DOI":"10.1109\/ISBI52829.2022.9761506"},{"key":"77_CR139","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics14171916","author":"DNC Kocakaya","year":"2024","unstructured":"Kocakaya DNC, \u00d6zel MB, Kartbak SBA, \u00c7akmak M, Sinano\u011flu EA (2024) Profile photograph classification performance of deep learning algorithms trained using cephalometric measurements: a preliminary study. Diagnostics. https:\/\/doi.org\/10.3390\/diagnostics14171916","journal-title":"Diagnostics"},{"issue":"1","key":"77_CR140","doi-asserted-by":"publisher","first-page":"64","DOI":"10.4103\/0973-029x.92976","volume":"16","author":"SS Kohli","year":"2012","unstructured":"Kohli SS, Kohli VS (2012) A comprehensive review of the genetic basis of cleft lip and palate. J Oral Maxillofac Pathol 16(1):64\u201372. https:\/\/doi.org\/10.4103\/0973-029x.92976","journal-title":"J Oral Maxillofac Pathol"},{"issue":"1","key":"77_CR141","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1186\/s40510-019-0295-8","volume":"20","author":"H K\u00f6k","year":"2019","unstructured":"K\u00f6k H, Acilar AM, \u0130zgi MS (2019) Usage and comparison of artificial intelligence algorithms for determination of growth and development by cervical vertebrae stages in orthodontics. Prog Orthod 20(1):41. https:\/\/doi.org\/10.1186\/s40510-019-0295-8","journal-title":"Prog Orthod"},{"issue":"Suppl 2","key":"77_CR142","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1111\/ocr.12443","volume":"24","author":"H K\u00f6k","year":"2021","unstructured":"K\u00f6k H, Izgi MS, Acilar AM (2021) Determination of growth and development periods in orthodontics with artificial neural network. Orthod Craniofac Res 24(Suppl 2):76\u201383. https:\/\/doi.org\/10.1111\/ocr.12443","journal-title":"Orthod Craniofac Res"},{"issue":"1","key":"77_CR143","doi-asserted-by":"publisher","first-page":"2","DOI":"10.5152\/TurkJOrthod.2020.20059","volume":"34","author":"H Kok","year":"2021","unstructured":"Kok H, Izgi MS, Acilar AM (2021) Evaluation of the artificial neural network and naive bayes models trained with vertebra ratios for growth and development determination. Turk J Orthod 34(1):2\u20139. https:\/\/doi.org\/10.5152\/TurkJOrthod.2020.20059","journal-title":"Turk J Orthod"},{"issue":"4","key":"77_CR144","doi-asserted-by":"publisher","first-page":"1846","DOI":"10.1007\/s10278-024-01061-6","volume":"37","author":"L Krenmayr","year":"2024","unstructured":"Krenmayr L, von Schwerin R, Schaudt D, Riedel P, Hafner A (2024) Dilatedtoothsegnet: tooth segmentation network on 3d dental meshes through increasing receptive vision. J Imaging Inform Med 37(4):1846\u20131862. https:\/\/doi.org\/10.1007\/s10278-024-01061-6","journal-title":"J Imaging Inform Med"},{"key":"77_CR145","doi-asserted-by":"publisher","unstructured":"K\u00fcchler, E. C., Kirschneck, C., Mara\u00f1\u00f3n-V\u00e1squez, G. A., Schroder, \u00c2. D., Baratto-Filho, F., Romano, F. L., . . . de Araujo, C. M. (2024). Mandibular and dental measurements for sex determination using machine learning. Sci Rep, 14(1), 9587. https:\/\/doi.org\/10.1038\/s41598-024-59556-9","DOI":"10.1038\/s41598-024-59556-9"},{"issue":"1","key":"77_CR146","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1007\/s00056-019-00203-8","volume":"81","author":"F Kunz","year":"2020","unstructured":"Kunz F, Stellzig-Eisenhauer A, Zeman F, Boldt J (2020) Artificial intelligence in orthodontics: evaluation of a fully automated cephalometric analysis using a customized convolutional neural network. J Orofac Orthop 81(1):52\u201368. https:\/\/doi.org\/10.1007\/s00056-019-00203-8","journal-title":"J Orofac Orthop"},{"key":"77_CR147","doi-asserted-by":"publisher","DOI":"10.1007\/s00056-023-00491-1","author":"F Kunz","year":"2023","unstructured":"Kunz F, Stellzig-Eisenhauer A, Widmaier LM, Zeman F, Boldt J (2023) Assessment of the quality of different commercial providers using artificial intelligence for automated cephalometric analysis compared to human orthodontic experts. J Orofac Orthop. https:\/\/doi.org\/10.1007\/s00056-023-00491-1","journal-title":"J Orofac Orthop"},{"key":"77_CR148","doi-asserted-by":"publisher","first-page":"21306","DOI":"10.1109\/ACCESS.2021.3052460","volume":"9","author":"HJ Kwon","year":"2021","unstructured":"Kwon HJ, Koo HI, Park J, Cho NI (2021) Multistage probabilistic approach for the localization of cephalometric landmarks. IEEE Access 9:21306\u201321314. https:\/\/doi.org\/10.1109\/ACCESS.2021.3052460","journal-title":"IEEE Access"},{"key":"77_CR149","doi-asserted-by":"publisher","unstructured":"Larkin A, Kim JS, Kim N, Baek SH, Yamada S, Park K, . . . Park JH (2024) Accuracy of artificial intelligence-assisted growth prediction in skeletal Class I preadolescent patients using serial lateral cephalograms for a 2-year growth interval. Orthod Craniofac Res. https:\/\/doi.org\/10.1111\/ocr.12764","DOI":"10.1111\/ocr.12764"},{"issue":"3","key":"77_CR150","doi-asserted-by":"publisher","DOI":"10.3390\/jpm12030387","volume":"12","author":"VNT Le","year":"2022","unstructured":"Le VNT, Kang J, Oh IS, Kim JG, Yang YM, Lee DW (2022) Effectiveness of human-artificial intelligence collaboration in cephalometric landmark detection. J Pers Med 12(3):387. https:\/\/doi.org\/10.3390\/jpm12030387","journal-title":"J Pers Med"},{"issue":"4","key":"77_CR151","doi-asserted-by":"publisher","first-page":"552","DOI":"10.1111\/ocr.12641","volume":"26","author":"L Leavitt","year":"2023","unstructured":"Leavitt L, Volovic J, Steinhauer L, Mason T, Eckert G, Dean JA, Dundar MM, Turkkahraman H (2023) Can we predict orthodontic extraction patterns by using machine learning? Orthod Craniofac Res 26(4):552\u2013559. https:\/\/doi.org\/10.1111\/ocr.12641","journal-title":"Orthod Craniofac Res"},{"issue":"1","key":"77_CR152","doi-asserted-by":"publisher","DOI":"10.1186\/s12903-020-01256-7","volume":"20","author":"JH Lee","year":"2020","unstructured":"Lee JH, Yu HJ, Kim MJ, Kim JW, Choi J (2020) Automated cephalometric landmark detection with confidence regions using Bayesian convolutional neural networks. BMC Oral Health 20(1):270. https:\/\/doi.org\/10.1186\/s12903-020-01256-7","journal-title":"BMC Oral Health"},{"issue":"1","key":"77_CR153","doi-asserted-by":"publisher","DOI":"10.1186\/s40510-022-00410-x","volume":"23","author":"SC Lee","year":"2022","unstructured":"Lee SC, Hwang HS, Lee KC (2022) Accuracy of deep learning-based integrated tooth models by merging intraoral scans and CBCT scans for 3d evaluation of root position during orthodontic treatment. Prog Orthod 23(1):15. https:\/\/doi.org\/10.1186\/s40510-022-00410-x","journal-title":"Prog Orthod"},{"issue":"1","key":"77_CR154","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-42870-z","volume":"13","author":"H Lee","year":"2023","unstructured":"Lee H, Cho JM, Ryu S, Ryu S, Chang E, Jung YS, Kim JY (2023a) Automatic identification of posteroanterior cephalometric landmarks using a novel deep learning algorithm: a comparative study with human experts. Sci Rep 13(1):15506. https:\/\/doi.org\/10.1038\/s41598-023-42870-z","journal-title":"Sci Rep"},{"issue":"6","key":"77_CR155","doi-asserted-by":"publisher","first-page":"106","DOI":"10.22514\/jocpd.2023.085","volume":"47","author":"J Lee","year":"2023","unstructured":"Lee J, Bae SR, Noh HK (2023b) Commercial artificial intelligence lateral cephalometric analysis: part 1-the possibility of replacing manual landmarking with artificial intelligence service. J Clin Pediatr Dent 47(6):106\u2013118. https:\/\/doi.org\/10.22514\/jocpd.2023.085","journal-title":"J Clin Pediatr Dent"},{"issue":"2","key":"77_CR156","doi-asserted-by":"publisher","first-page":"144","DOI":"10.2319\/031622-225.1","volume":"93","author":"S Lee","year":"2023","unstructured":"Lee S, Wu TH, Deguchi T, Ni A, Lu WE, Minhas S, Murphy S, Ko CC (2023c) Assessment of malalignment factors related to Invisalign treatment time aided by automated imaging processes. Angle Orthod 93(2):144\u2013150. https:\/\/doi.org\/10.2319\/031622-225.1","journal-title":"Angle Orthod"},{"issue":"2","key":"77_CR157","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1053\/j.sodo.2021.05.002","volume":"27","author":"R Leonardi","year":"2021","unstructured":"Leonardi R, Lo Giudice A, Isola G, Spampinato C (2021) Deep learning and computer vision: two promising pillars, powering the future in orthodontics. Semin Orthod 27(2):62\u201368. https:\/\/doi.org\/10.1053\/j.sodo.2021.05.002","journal-title":"Semin Orthod"},{"issue":"1","key":"77_CR158","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-018-38439-w","volume":"9","author":"P Li","year":"2019","unstructured":"Li P, Kong D, Tang T, Su D, Yang P, Wang H, Zhao Z, Liu Y (2019) Orthodontic treatment planning based on artificial neural networks. Sci Rep 9(1):2037. https:\/\/doi.org\/10.1038\/s41598-018-38439-w","journal-title":"Sci Rep"},{"issue":"5","key":"77_CR159","doi-asserted-by":"publisher","first-page":"905","DOI":"10.3233\/xst-200678","volume":"28","author":"Q Li","year":"2020","unstructured":"Li Q, Chen K, Han L, Zhuang Y, Li J, Lin J (2020) Automatic tooth roots segmentation of cone beam computed tomography image sequences using U-net and RNN. J Xray Sci Technol 28(5):905\u2013922. https:\/\/doi.org\/10.3233\/xst-200678","journal-title":"J Xray Sci Technol"},{"issue":"6","key":"77_CR160","doi-asserted-by":"publisher","DOI":"10.1259\/dmfr.20220070","volume":"51","author":"H Li","year":"2022","unstructured":"Li H, Chen Y, Wang Q, Gong X, Lei Y, Tian J, Gao X (2022a) Convolutional neural network-based automatic cervical vertebral maturation classification method. Dentomaxillofac Radiol 51(6):20220070. https:\/\/doi.org\/10.1259\/dmfr.20220070","journal-title":"Dentomaxillofac Radiol"},{"key":"77_CR161","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics12061359","author":"H Li","year":"2022","unstructured":"Li H, Xu Y, Lei Y, Wang Q, Gao X (2022b) Automatic classification for sagittal craniofacial patterns based on different convolutional neural networks. Diagnostics. https:\/\/doi.org\/10.3390\/diagnostics12061359","journal-title":"Diagnostics"},{"issue":"1","key":"77_CR162","doi-asserted-by":"publisher","DOI":"10.1186\/s12903-023-03266-7","volume":"23","author":"H Li","year":"2023","unstructured":"Li H, Li H, Yuan L, Liu C, Xiao S, Liu Z, Zhou G, Dong T, Ouyang N, Liu Lu, Ma C, Feng Y, Zheng Y, Xia L, Fang B (2023) The psc-CVM assessment system: a three-stage type system for CVM assessment based on deep learning. BMC Oral Health 23(1):557. https:\/\/doi.org\/10.1186\/s12903-023-03266-7","journal-title":"BMC Oral Health"},{"issue":"1","key":"77_CR163","doi-asserted-by":"publisher","DOI":"10.1007\/s00784-023-05440-1","volume":"28","author":"R Li","year":"2024","unstructured":"Li R, Zhu C, Chu F, Yu Q, Fan D, Ouyang N, Fang B (2024) Deep learning for virtual orthodontic bracket removal: tool establishment and application. Clin Oral Investig 28(1):121. https:\/\/doi.org\/10.1007\/s00784-023-05440-1","journal-title":"Clin Oral Investig"},{"issue":"8","key":"77_CR164","doi-asserted-by":"publisher","first-page":"4325","DOI":"10.1109\/jbhi.2022.3179619","volume":"26","author":"N Liao","year":"2022","unstructured":"Liao N, Dai J, Tang Y, Zhong Q, Mo S (2022) ICVM: an interpretable deep learning model for CVM assessment under label uncertainty. IEEE J Biomed Health Inform 26(8):4325\u20134334. https:\/\/doi.org\/10.1109\/jbhi.2022.3179619","journal-title":"IEEE J Biomed Health Inform"},{"issue":"1","key":"77_CR165","doi-asserted-by":"publisher","DOI":"10.1186\/s12903-021-01983-5","volume":"21","author":"HK Lim","year":"2021","unstructured":"Lim HK, Jung SK, Kim SH, Cho Y, Song IS (2021) Deep semi-supervised learning for automatic segmentation of inferior alveolar nerve using a convolutional neural network. BMC Oral Health 21(1):630. https:\/\/doi.org\/10.1186\/s12903-021-01983-5","journal-title":"BMC Oral Health"},{"issue":"2","key":"77_CR166","doi-asserted-by":"publisher","first-page":"616","DOI":"10.1097\/scs.0000000000006943","volume":"32","author":"G Lin","year":"2021","unstructured":"Lin G, Kim PJ, Baek SH, Kim HG, Kim SW, Chung JH (2021) Early prediction of the need for Orthognathic Surgery in patients with repaired unilateral cleft lip and palate using machine learning and longitudinal lateral cephalometric analysis data. J Craniofac Surg 32(2):616\u2013620. https:\/\/doi.org\/10.1097\/scs.0000000000006943","journal-title":"J Craniofac Surg"},{"issue":"3","key":"77_CR167","doi-asserted-by":"publisher","DOI":"10.1259\/dmfr.20210341","volume":"51","author":"B Lin","year":"2022","unstructured":"Lin B, Cheng M, Wang S, Li F, Zhou Q (2022) Automatic detection of anteriorly displaced temporomandibular joint discs on magnetic resonance images using a deep learning algorithm. Dentomaxillofac Radiol 51(3):20210341. https:\/\/doi.org\/10.1259\/dmfr.20210341","journal-title":"Dentomaxillofac Radiol"},{"issue":"2","key":"77_CR168","doi-asserted-by":"publisher","first-page":"467","DOI":"10.1109\/tmi.2022.3222388","volume":"42","author":"Z Liu","year":"2023","unstructured":"Liu Z, He X, Wang H, Xiong H, Zhang Y, Wang G, Hao J, Feng Y, Zhu F, Hu H (2023) Hierarchical self-supervised learning for 3d tooth segmentation in intra-oral mesh scans. IEEE Trans Med Imaging 42(2):467\u2013480. https:\/\/doi.org\/10.1109\/tmi.2022.3222388","journal-title":"IEEE Trans Med Imaging"},{"key":"77_CR169","doi-asserted-by":"publisher","DOI":"10.2196\/67378","volume":"27","author":"C Liu","year":"2025","unstructured":"Liu C, Liu Y, Yi C, Xie T, Tian J, Deng P, Shan Y, Dong H, Xu Y (2025) Application of a 3D fusion model to evaluate the efficacy of clear aligner therapy in malocclusion patients: prospective observational study. J Med Internet Res 27:e67378. https:\/\/doi.org\/10.2196\/67378","journal-title":"J Med Internet Res"},{"key":"77_CR170","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1111\/ocr.12536","volume":"24","author":"A Lo Giudice","year":"2021","unstructured":"Lo Giudice A, Ronsivalle V, Spampinato C, Leonardi R (2021) Fully automatic segmentation of the mandible based on convolutional neural networks (CNNs). Orthod Craniofac Res 24:100\u2013107. https:\/\/doi.org\/10.1111\/ocr.12536","journal-title":"Orthod Craniofac Res"},{"key":"77_CR171","doi-asserted-by":"publisher","DOI":"10.3390\/app11062634","author":"M Machoy","year":"2021","unstructured":"Machoy M, Szyszka-Sommerfeld L, Koprowski R, Wawrzyk A, Wo\u017aniak K, Wilczy\u0144ski S (2021) Assessment of periodontium temperature changes under orthodontic force by using objective and automatic classifier. Appl Sci. https:\/\/doi.org\/10.3390\/app11062634","journal-title":"Appl Sci"},{"issue":"1","key":"77_CR172","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1186\/s12903-022-02170-w","volume":"22","author":"RK Mahto","year":"2022","unstructured":"Mahto RK, Kafle D, Giri A, Luintel S, Karki A (2022) Evaluation of fully automated cephalometric measurements obtained from web-based artificial intelligence driven platform. BMC Oral Health 22(1):132. https:\/\/doi.org\/10.1186\/s12903-022-02170-w","journal-title":"BMC Oral Health"},{"key":"77_CR173","doi-asserted-by":"publisher","DOI":"10.3390\/e21121222","author":"M Makaremi","year":"2019","unstructured":"Makaremi M, Lacaule C, Mohammad-Djafari A (2019) Deep learning and artificial intelligence for the determination of the cervical vertebra maturation degree from lateral radiography. Entropy. https:\/\/doi.org\/10.3390\/e21121222","journal-title":"Entropy"},{"key":"77_CR174","doi-asserted-by":"publisher","unstructured":"Makaremi M, Vafaei Sadr A, Marcy B, Chraibi Kaadoud I, Mohammad-Djafari A, Sadoun S, . . . N'Kaoua B (2023) An interpretable machine learning approach to study the relationship beetwen retrognathia and skull anatomy. Sci Rep 13(1):18130. https:\/\/doi.org\/10.1038\/s41598-023-45314-w","DOI":"10.1038\/s41598-023-45314-w"},{"issue":"5","key":"77_CR175","doi-asserted-by":"publisher","first-page":"1383","DOI":"10.1097\/SCS.0b013e3181b0daa3","volume":"20","author":"F Manna","year":"2009","unstructured":"Manna F, Pensiero S, Clarich G, Guarneri GF, Parodi PC (2009) Cleft lip and palate: current status from the literature and our experience. J Craniofac Surg 20(5):1383\u20131387. https:\/\/doi.org\/10.1097\/SCS.0b013e3181b0daa3","journal-title":"J Craniofac Surg"},{"issue":"Suppl 2","key":"77_CR176","doi-asserted-by":"publisher","first-page":"S234","DOI":"10.4103\/0975-7406.100216","volume":"4","author":"R Manne","year":"2012","unstructured":"Manne R, Gandikota C, Juvvadi SR, Rama HR, Anche S (2012) Impacted canines: etiology, diagnosis, and orthodontic management. J Pharm Bioallied Sci 4(Suppl 2):S234-238. https:\/\/doi.org\/10.4103\/0975-7406.100216","journal-title":"J Pharm Bioallied Sci"},{"key":"77_CR177","doi-asserted-by":"publisher","unstructured":"Mario MC, Abe JM, Ortega NR, Del Santo M Jr (2010) Paraconsistent artificial neural network as auxiliary in cephalometric diagnosis. Artif Organs 34(7):E215\u2013221. https:\/\/doi.org\/10.1111\/j.1525-1594.2010.00994.x","DOI":"10.1111\/j.1525-1594.2010.00994.x"},{"issue":"3","key":"77_CR178","doi-asserted-by":"publisher","DOI":"10.1016\/j.ortho.2023.100759","volume":"21","author":"T Mason","year":"2023","unstructured":"Mason T, Kelly KM, Eckert G, Dean JA, Dundar MM, Turkkahraman H (2023) A machine learning model for orthodontic extraction\/non-extraction decision in a racially and ethnically diverse patient population. Int Orthod 21(3):100759. https:\/\/doi.org\/10.1016\/j.ortho.2023.100759","journal-title":"Int Orthod"},{"issue":"3","key":"77_CR179","doi-asserted-by":"publisher","first-page":"142","DOI":"10.5152\/TurkJOrthod.2020.20062","volume":"33","author":"P Meri\u00e7","year":"2020","unstructured":"Meri\u00e7 P, Naoumova J (2020) Web-based fully automated cephalometric analysis: comparisons between App-aided, computerized, and manual tracings. Turk J Orthod 33(3):142\u2013149. https:\/\/doi.org\/10.5152\/TurkJOrthod.2020.20062","journal-title":"Turk J Orthod"},{"key":"77_CR180","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics14020196","author":"S Minhas","year":"2024","unstructured":"Minhas S, Wu TH, Kim DG, Chen S, Wu YC, Ko CC (2024) Artificial intelligence for 3D reconstruction from 2D panoramic X-rays to assess maxillary impacted canines. Diagnostics. https:\/\/doi.org\/10.3390\/diagnostics14020196","journal-title":"Diagnostics"},{"issue":"2","key":"77_CR181","doi-asserted-by":"publisher","first-page":"112","DOI":"10.4041\/kjod.2022.52.2.112","volume":"52","author":"H Mohammad-Rahimi","year":"2022","unstructured":"Mohammad-Rahimi H, Motamadian SR, Nadimi M, Hassanzadeh-Samani S, Minabi MAS, Mahmoudinia E, Lee VY, Rohban MH (2022) Deep learning for the classification of cervical maturation degree and pubertal growth spurts: a pilot study. Korean J Orthod 52(2):112\u2013122. https:\/\/doi.org\/10.4041\/kjod.2022.52.2.112","journal-title":"Korean J Orthod"},{"issue":"12","key":"77_CR182","doi-asserted-by":"publisher","first-page":"1173","DOI":"10.1111\/joor.13378","volume":"49","author":"H Mohammed","year":"2022","unstructured":"Mohammed H, Kumar R Jr., Bennani H, Halberstadt JB, Farella M (2022) Automated detection of smiles as discrete episodes. J Oral Rehabil 49(12):1173\u20131180. https:\/\/doi.org\/10.1111\/joor.13378","journal-title":"J Oral Rehabil"},{"issue":"3","key":"77_CR183","doi-asserted-by":"publisher","first-page":"449","DOI":"10.1016\/j.ajodo.2017.06.028","volume":"153","author":"J Mont\u00fafar","year":"2018","unstructured":"Mont\u00fafar J, Romero M, Scougall-Vilchis RJ (2018) Automatic 3-dimensional cephalometric landmarking based on active shape models in related projections. Am J Orthod Dentofacial Orthop 153(3):449\u2013458","journal-title":"Am J Orthod Dentofacial Orthop"},{"issue":"6","key":"77_CR184","doi-asserted-by":"publisher","first-page":"823","DOI":"10.2319\/021920-116.1","volume":"90","author":"J-H Moon","year":"2020","unstructured":"Moon J-H, Hwang H-W, Yu Y, Kim M-G, Donatelli RE, Lee S-J (2020) How much deep learning is enough for automatic identification to be reliable?: A cephalometric example. Angle Orthod 90(6):823\u2013830. https:\/\/doi.org\/10.2319\/021920-116.1","journal-title":"Angle Orthod"},{"issue":"2","key":"77_CR185","doi-asserted-by":"publisher","first-page":"207","DOI":"10.2319\/031723-181.1","volume":"94","author":"JH Moon","year":"2024","unstructured":"Moon JH, Shin HK, Lee JM, Cho SJ, Park JA, Donatelli RE, Lee SJ (2024) Comparison of individualized facial growth prediction models based on the partial least squares and artificial intelligence. Angle Orthod 94(2):207\u2013215. https:\/\/doi.org\/10.2319\/031723-181.1","journal-title":"Angle Orthod"},{"key":"77_CR186","doi-asserted-by":"publisher","DOI":"10.3390\/jcm13206074","author":"T Mourgues","year":"2024","unstructured":"Mourgues T, Gonz\u00e1lez-Olmo MJ, Huanca Ghislanzoni L, Pe\u00f1acoba C, Romero-Maroto M (2024) Artificial intelligence in aesthetic dentistry: is treatment with aligners clinically realistic? J Clin Med. https:\/\/doi.org\/10.3390\/jcm13206074","journal-title":"J Clin Med"},{"key":"77_CR187","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics13081485","author":"O Moztarzadeh","year":"2023","unstructured":"Moztarzadeh O, Jamshidi M, Sargolzaei S, Keikhaee F, Jamshidi A, Shadroo S, Hauer L (2023) Metaverse and medical diagnosis: a blockchain-based digital twinning approach based on MobileNetV2 algorithm for cervical vertebral maturation. Diagnostics. https:\/\/doi.org\/10.3390\/diagnostics13081485","journal-title":"Diagnostics"},{"key":"77_CR188","doi-asserted-by":"publisher","DOI":"10.1097\/scs.0000000000010346","author":"G Mu\u00f1oz","year":"2024","unstructured":"Mu\u00f1oz G, Zamora D, Brito L, Ravelo V, de Moraes M, Olate S (2024) Comparison between an expert operator an inexperienced operator, and artificial intelligence software: a brief clinical study of cephalometric diagnostic. J Craniofac Surg. https:\/\/doi.org\/10.1097\/scs.0000000000010346","journal-title":"J Craniofac Surg"},{"key":"77_CR189","unstructured":"Muraev AA, Tsai P, Kibardin I, Oborotistov N, Shirayeva T, Ivanov S, . . . Tuturov N (2020) Frontal cephalometric landmarking: humans vs artificial neural networks. Int J Comput Dent 23(2):139\u2013148."},{"issue":"1","key":"77_CR190","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/j.ajodo.2022.10.024","volume":"164","author":"SJ Murphy","year":"2023","unstructured":"Murphy SJ, Lee S, Scharm JC, Kim S, Amin AA, Wu TH, Deguchi T (2023) Comparison of maxillary anterior tooth movement between Invisalign and fixed appliances. Am J Orthod Dentofacial Orthop 164(1):24\u201333. https:\/\/doi.org\/10.1016\/j.ajodo.2022.10.024","journal-title":"Am J Orthod Dentofacial Orthop"},{"issue":"4","key":"77_CR191","doi-asserted-by":"publisher","first-page":"248","DOI":"10.1034\/j.1399-0004.2002.610402.x","volume":"61","author":"JC Murray","year":"2002","unstructured":"Murray JC (2002) Gene\/environment causes of cleft lip and\/or palate. Clin Genet 61(4):248\u2013256. https:\/\/doi.org\/10.1034\/j.1399-0004.2002.610402.x","journal-title":"Clin Genet"},{"issue":"Suppl 2","key":"77_CR192","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1111\/ocr.12521","volume":"24","author":"N Nauwelaers","year":"2021","unstructured":"Nauwelaers N, Matthews H, Fan Y, Croquet B, Hoskens H, Mahdi S, El Sergani A, Gong S, Xu T, Bronstein M, Marazita M, Weinberg S, Claes P (2021) Exploring palatal and dental shape variation with 3d shape analysis and geometric deep learning. Orthod Craniofac Res 24(Suppl 2):134\u2013143. https:\/\/doi.org\/10.1111\/ocr.12521","journal-title":"Orthod Craniofac Res"},{"key":"77_CR193","doi-asserted-by":"publisher","DOI":"10.1016\/j.jdent.2024.104931","volume":"144","author":"FD Ni","year":"2024","unstructured":"Ni FD, Xu ZN, Liu MQ, Zhang MJ, Li S, Bai HL, Ding P, Fu KY (2024) Towards clinically applicable automated mandibular canal segmentation on CBCT. J Dent 144:104931. https:\/\/doi.org\/10.1016\/j.jdent.2024.104931","journal-title":"J Dent"},{"issue":"1","key":"77_CR194","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1097\/scs.0000000000004901","volume":"30","author":"S Nishimoto","year":"2019","unstructured":"Nishimoto S, Sotsuka Y, Kawai K, Ishise H, Kakibuchi M (2019) Personal computer-based cephalometric landmark detection with deep learning, using cephalograms on the internet. J Craniofac Surg 30(1):91\u201395. https:\/\/doi.org\/10.1097\/scs.0000000000004901","journal-title":"J Craniofac Surg"},{"issue":"1","key":"77_CR195","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1186\/s13005-024-00448-8","volume":"20","author":"B Noeldeke","year":"2024","unstructured":"Noeldeke B, Vassis S, Sefidroodi M, Pauwels R, Stoustrup P (2024) Comparison of deep learning models to detect crossbites on 2D intraoral photographs. Head Face Med 20(1):45. https:\/\/doi.org\/10.1186\/s13005-024-00448-8","journal-title":"Head Face Med"},{"issue":"3","key":"77_CR196","doi-asserted-by":"publisher","first-page":"1133","DOI":"10.1007\/s00784-022-04708-2","volume":"27","author":"F Nogueira-Reis","year":"2023","unstructured":"Nogueira-Reis F, Morgan N, Nomidis S, Van Gerven A, Oliveira-Santos N, Jacobs R, Tabchoury CPM (2023) Three-dimensional maxillary virtual patient creation by convolutional neural network-based segmentation on cone-beam computed tomography images. Clin Oral Investig 27(3):1133\u20131141. https:\/\/doi.org\/10.1007\/s00784-022-04708-2","journal-title":"Clin Oral Investig"},{"issue":"2","key":"77_CR197","doi-asserted-by":"publisher","first-page":"306","DOI":"10.1016\/j.oooo.2024.02.017","volume":"138","author":"F Nogueira-Reis","year":"2024","unstructured":"Nogueira-Reis F, Cascante-Sequeira D, Farias-Gomes A, de Macedo MMG, Watanabe RN, Santiago AG, Tabchoury CPM, Freitas DQ (2024) Determination of the pubertal growth spurt by artificial intelligence analysis of cervical vertebrae maturation in lateral cephalometric radiographs. Oral Surg Oral Med Oral Pathol Oral Radiol 138(2):306\u2013315. https:\/\/doi.org\/10.1016\/j.oooo.2024.02.017","journal-title":"Oral Surg Oral Med Oral Pathol Oral Radiol"},{"issue":"4","key":"77_CR198","doi-asserted-by":"publisher","first-page":"445","DOI":"10.1093\/ejo\/cjac016","volume":"44","author":"D Obwegeser","year":"2022","unstructured":"Obwegeser D, Timofte R, Mayer C, Eliades T, Bornstein MM, Sch\u00e4tzle MA, Patcas R (2022) Using artificial intelligence to determine the influence of dental aesthetics on facial attractiveness in comparison to other facial modifications. Eur J Orthod 44(4):445\u2013451. https:\/\/doi.org\/10.1093\/ejo\/cjac016","journal-title":"Eur J Orthod"},{"issue":"4","key":"77_CR199","doi-asserted-by":"publisher","DOI":"10.1016\/j.ortho.2024.100926","volume":"22","author":"K O'Friel","year":"2024","unstructured":"O\u2019Friel K, Chapple A, Ballard R, Armbruster P (2024) Assessing AudaxCeph\u00ae\u2019s cephalometric tracing technology versus a semi-automated approach for analyzing severe Class II and Class III skeletons. Int Orthod 22(4):100926. https:\/\/doi.org\/10.1016\/j.ortho.2024.100926","journal-title":"Int Orthod"},{"issue":"5","key":"77_CR200","doi-asserted-by":"publisher","first-page":"669","DOI":"10.1016\/j.ajodo.2011.02.025","volume":"140","author":"JA Olsen","year":"2011","unstructured":"Olsen JA, Inglehart MR (2011) Malocclusions and perceptions of attractiveness, intelligence, and personality, and behavioral intentions. Am J Orthod Dentofacial Orthop 140(5):669\u2013679. https:\/\/doi.org\/10.1016\/j.ajodo.2011.02.025","journal-title":"Am J Orthod Dentofacial Orthop"},{"issue":"1","key":"77_CR201","first-page":"5","volume":"5","author":"ZA Omar","year":"2018","unstructured":"Omar ZA, Chin SN, Sentian A, Hamzah N, Yassin F (2018) Exploring contributing features of pre-graft orthodontic treatment of cleft lip and palate patients using random forests. Transac Sci Technol 5(1):5\u201311","journal-title":"Transac Sci Technol"},{"key":"77_CR202","doi-asserted-by":"publisher","first-page":"S3","DOI":"10.1016\/j.identj.2024.07.578","volume":"74","author":"M \u00d6zcan","year":"2024","unstructured":"\u00d6zcan M, Erdem B, Turan B, Tokatl\u0131 N, \u015ear \u00c7, \u00d6zdemir F (2024) Deep-learning model for assessing difficulty in localizing impacted canines. Int Dent J 74:S3. https:\/\/doi.org\/10.1016\/j.identj.2024.07.578","journal-title":"Int Dent J"},{"issue":"1","key":"77_CR203","doi-asserted-by":"publisher","DOI":"10.1186\/s12903-024-04262-1","volume":"24","author":"PS Ozsunkar","year":"2024","unstructured":"Ozsunkar PS, \u00d6zen D, Abdelkarim AZ, Duman S, U\u011furlu M, Dem\u0130r MR, \u00d6zen D\u00c7el\u0130k, Kuleli B, \u00c7el\u0130k \u00d6, Imamoglu BS, Bayrakdar IS, Duman SB (2024) Detecting white spot lesions on post-orthodontic oral photographs using deep learning based on the YOLOv5x algorithm: a pilot study. BMC Oral Health 24(1):490. https:\/\/doi.org\/10.1186\/s12903-024-04262-1","journal-title":"BMC Oral Health"},{"key":"77_CR204","doi-asserted-by":"publisher","DOI":"10.3390\/app13126921","author":"S Panesar","year":"2023","unstructured":"Panesar S, Zhao A, Hollensbe E, Wong A, Bhamidipalli SS, Eckert G, Dutra V, Turkkahraman H (2023) Precision and accuracy assessment of cephalometric analyses performed by deep learning artificial intelligence with and without human augmentation. Appl Sci. https:\/\/doi.org\/10.3390\/app13126921","journal-title":"Appl Sci"},{"issue":"4","key":"77_CR205","doi-asserted-by":"publisher","first-page":"464","DOI":"10.1016\/j.ajodo.2018.10.021","volume":"156","author":"MA Papio","year":"2019","unstructured":"Papio MA, Fields HW Jr, Beck FM, Firestone AR, Rosenstiel SF (2019) The effect of dental and background facial attractiveness on facial attractiveness and perceived integrity and social and intellectual qualities. Am J Orthod Dentofacial Orthop 156(4):464-474.e461. https:\/\/doi.org\/10.1016\/j.ajodo.2018.10.021","journal-title":"Am J Orthod Dentofacial Orthop"},{"issue":"6","key":"77_CR206","doi-asserted-by":"publisher","first-page":"903","DOI":"10.2319\/022019-127.1","volume":"89","author":"J-H Park","year":"2019","unstructured":"Park J-H, Hwang H-W, Moon J-H, Yu Y, Kim H, Her S-B, Srinivasan G, Aljanabi MNA, Donatelli RE, Lee S-J (2019a) Automated identification of cephalometric landmarks: Part 1\u2014comparisons between the latest deep-learning methods YOLOV3 and SSD. Angle Orthod 89(6):903\u2013909","journal-title":"Angle Orthod"},{"issue":"6","key":"77_CR207","doi-asserted-by":"publisher","first-page":"903","DOI":"10.2319\/022019-127.1","volume":"89","author":"JH Park","year":"2019","unstructured":"Park JH, Hwang HW, Moon JH, Yu Y, Kim H, Her SB, Srinivasan G, Aljanabi MNA, Donatelli RE, Lee SJ (2019b) Automated identification of cephalometric landmarks: Part 1: comparisons between the latest deep learning methods YOLOV3 and SSD. Angle Orthod 89(6):903\u2013909. https:\/\/doi.org\/10.2319\/022019-127.1","journal-title":"Angle Orthod"},{"issue":"2","key":"77_CR208","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1053\/j.sodo.2021.05.005","volume":"27","author":"JH Park","year":"2021","unstructured":"Park JH, Kim YJ, Kim J, Kim J, Kim IH, Kim N, Vaid NR, Kook YA (2021) Use of artificial intelligence to predict outcomes of nonextraction treatment of class II malocclusions. Semin Orthod 27(2):87\u201395. https:\/\/doi.org\/10.1053\/j.sodo.2021.05.005","journal-title":"Semin Orthod"},{"issue":"11","key":"77_CR209","doi-asserted-by":"publisher","first-page":"1372","DOI":"10.1177\/00220345221106676","volume":"101","author":"YS Park","year":"2022","unstructured":"Park YS, Choi JH, Kim Y, Choi SH, Lee JH, Kim KH, Chung CJ (2022) Deep learning-based prediction of the 3D postorthodontic facial changes. J Dent Res 101(11):1372\u20131379. https:\/\/doi.org\/10.1177\/00220345221106676","journal-title":"J Dent Res"},{"key":"77_CR210","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics13172729","author":"M Parrish","year":"2023","unstructured":"Parrish M, O\u2019Connell E, Eckert G, Hughes J, Badirli S, Turkkahraman H (2023) Short- and long-term prediction of the post-pubertal mandibular length and Y-axis in females utilizing machine learning. Diagnostics. https:\/\/doi.org\/10.3390\/diagnostics13172729","journal-title":"Diagnostics"},{"issue":"1","key":"77_CR211","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1016\/j.ijom.2018.07.010","volume":"48","author":"R Patcas","year":"2019","unstructured":"Patcas R, Bernini DAJ, Volokitin A, Agustsson E, Rothe R, Timofte R (2019a) Applying artificial intelligence to assess the impact of orthognathic treatment on facial attractiveness and estimated age. Int J Oral Maxillofac Surg 48(1):77\u201383. https:\/\/doi.org\/10.1016\/j.ijom.2018.07.010","journal-title":"Int J Oral Maxillofac Surg"},{"issue":"4","key":"77_CR212","doi-asserted-by":"publisher","first-page":"428","DOI":"10.1093\/ejo\/cjz007","volume":"41","author":"R Patcas","year":"2019","unstructured":"Patcas R, Timofte R, Volokitin A, Agustsson E, Eliades T, Eichenberger M, Bornstein MM (2019b) Facial attractiveness of cleft patients: a direct comparison between artificial-intelligence-based scoring and conventional rater groups. Eur J Orthod 41(4):428\u2013433. https:\/\/doi.org\/10.1093\/ejo\/cjz007","journal-title":"Eur J Orthod"},{"issue":"2","key":"77_CR213","doi-asserted-by":"publisher","first-page":"1013","DOI":"10.1007\/s00266-021-02537-4","volume":"46","author":"CJ Peck","year":"2022","unstructured":"Peck CJ, Patel VK, Parsaei Y, Pourtaheri N, Allam O, Lopez J, Steinbacher D (2022) Commercial artificial intelligence software as a tool for assessing facial attractiveness: a proof-of-concept study in an orthognathic surgery cohort. Aesthet Plast Surg 46(2):1013\u20131016. https:\/\/doi.org\/10.1007\/s00266-021-02537-4","journal-title":"Aesthet Plast Surg"},{"key":"77_CR214","doi-asserted-by":"crossref","unstructured":"Pei Y, Qin H, Ma G, Guo Y, Chen G, Xu T, Zha H (2017) Multi-scale volumetric convnet with nested residual connections for segmentation of anterior cranial base. Paper presented at the lecture notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","DOI":"10.1007\/978-3-319-67389-9_15"},{"key":"77_CR215","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajodo.2024.03.011","author":"J Peng","year":"2024","unstructured":"Peng J, Chen S, Shang F, Yang Y, Jiang R (2024) Measurement plane of the cross-sectional area of the masseter muscle in patients with skeletal class III malocclusion: an artificial intelligence model. Am J Orthod Dentofacial Orthop. https:\/\/doi.org\/10.1016\/j.ajodo.2024.03.011","journal-title":"Am J Orthod Dentofacial Orthop"},{"issue":"2","key":"77_CR216","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1053\/j.sodo.2021.05.006","volume":"27","author":"L Perillo","year":"2021","unstructured":"Perillo L, Auconi P, d\u2019Apuzzo F, Grassia V, Scazzocchio M, Nucci L, Franchi L (2021) Machine learning in the prognostic appraisal of class III growth. Semin Orthod 27(2):96\u2013108. https:\/\/doi.org\/10.1053\/j.sodo.2021.05.006","journal-title":"Semin Orthod"},{"issue":"1","key":"77_CR217","doi-asserted-by":"publisher","DOI":"10.1186\/s12903-023-02984-2","volume":"23","author":"T Popova","year":"2023","unstructured":"Popova T, Stocker T, Khazaei Y, Malenova Y, Wichelhaus A, Sabbagh H (2023) Influence of growth structures and fixed appliances on automated cephalometric landmark recognition with a customized convolutional neural network. BMC Oral Health 23(1):274. https:\/\/doi.org\/10.1186\/s12903-023-02984-2","journal-title":"BMC Oral Health"},{"key":"77_CR218","doi-asserted-by":"publisher","unstructured":"Prasad J, Mallikarjunaiah DR, Shetty A, Gandedkar N, Chikkamuniswamy AB, Shivashankar PC (2023) Machine learning predictive model as clinical decision support system in orthodontic treatment planning. Dent J 11(1). https:\/\/doi.org\/10.3390\/dj11010001","DOI":"10.3390\/dj11010001"},{"key":"77_CR219","doi-asserted-by":"publisher","DOI":"10.1016\/j.jdent.2022.104238","volume":"124","author":"F Preda","year":"2022","unstructured":"Preda F, Morgan N, Van Gerven A, Nogueira-Reis F, Smolders A, Wang X, Nomidis S, Shaheen E, Willems H, Jacobs R (2022) Deep convolutional neural network-based automated segmentation of the maxillofacial complex from cone-beam computed tomography: a validation study. J Dent 124:104238. https:\/\/doi.org\/10.1016\/j.jdent.2022.104238","journal-title":"J Dent"},{"issue":"1","key":"77_CR220","doi-asserted-by":"publisher","DOI":"10.1590\/2177-6709.28.1.e2321214.oar","volume":"28","author":"STT Prince","year":"2023","unstructured":"Prince STT, Srinivasan D, Duraisamy S, Kannan R, Rajaram K (2023) Reproducibility of linear and angular cephalometric measurements obtained by an artificial-intelligence assisted software (WebCeph) in comparison with digital software (AutoCEPH) and manual tracing method. Dent Press J Orthod 28(1):e2321214. https:\/\/doi.org\/10.1590\/2177-6709.28.1.e2321214.oar","journal-title":"Dent Press J Orthod"},{"issue":"3","key":"77_CR221","doi-asserted-by":"publisher","first-page":"349","DOI":"10.1111\/ocr.12615","volume":"26","author":"MT Radwan","year":"2023","unstructured":"Radwan MT, Sin \u00c7, Akkaya N, Vahdettin L (2023) Artificial intelligence-based algorithm for cervical vertebrae maturation stage assessment. Orthod Craniofac Res 26(3):349\u2013355. https:\/\/doi.org\/10.1111\/ocr.12615","journal-title":"Orthod Craniofac Res"},{"issue":"26","key":"77_CR222","doi-asserted-by":"publisher","first-page":"37403","DOI":"10.1007\/s11042-021-11596-3","volume":"81","author":"RA Ramadan","year":"2022","unstructured":"Ramadan RA, Khedr AY, Yadav K, Alreshidi EJ, Sharif MH, Azar AT, Kamberaj H (2022) Convolution neural network based automatic localization of landmarks on lateral x-ray images. Multimed Tools Appl 81(26):37403\u201337415. https:\/\/doi.org\/10.1007\/s11042-021-11596-3","journal-title":"Multimed Tools Appl"},{"issue":"4","key":"77_CR223","doi-asserted-by":"publisher","first-page":"264","DOI":"10.25259\/APOS_270_2023","volume":"14","author":"N Ramasubbu","year":"2024","unstructured":"Ramasubbu N, Valai Kasim SA, Thavarajah R, Nathamuni Rengarajan K (2024) Applying artificial intelligence to predict the outcome of orthodontic treatment. APOS Trends Orthod 14(4):264\u2013272. https:\/\/doi.org\/10.25259\/APOS_270_2023","journal-title":"APOS Trends Orthod"},{"key":"77_CR224","doi-asserted-by":"publisher","DOI":"10.3390\/medicina59111973","author":"AM Rauf","year":"2023","unstructured":"Rauf AM, Mahmood TMA, Mohammed MH, Omer ZQ, Kareem FA (2023) Orthodontic implementation of machine learning algorithms for predicting some linear dental arch measurements and preventing anterior segment malocclusion: a prospective study. Medicina (Kaunas). https:\/\/doi.org\/10.3390\/medicina59111973","journal-title":"Medicina (Kaunas)"},{"issue":"2","key":"77_CR225","doi-asserted-by":"publisher","first-page":"102","DOI":"10.4041\/kjod.2022.52.2.102","volume":"52","author":"AD Real","year":"2022","unstructured":"Real AD, Real OD, Sardina S, Oyonarte R (2022) Use of automated artificial intelligence to predict the need for orthodontic extractions. Korean J Orthod 52(2):102\u2013111. https:\/\/doi.org\/10.4041\/kjod.2022.52.2.102","journal-title":"Korean J Orthod"},{"issue":"4","key":"77_CR226","doi-asserted-by":"publisher","first-page":"472","DOI":"10.1016\/j.ajodo.2014.12.016","volume":"147","author":"MR Richards","year":"2015","unstructured":"Richards MR, Fields HW Jr., Beck FM, Firestone AR, Walther DB, Rosenstiel S, Sacksteder JM (2015) Contribution of malocclusion and female facial attractiveness to smile esthetics evaluated by eye tracking. Am J Orthod Dentofacial Orthop 147(4):472\u2013482. https:\/\/doi.org\/10.1016\/j.ajodo.2014.12.016","journal-title":"Am J Orthod Dentofacial Orthop"},{"issue":"4","key":"77_CR227","doi-asserted-by":"publisher","DOI":"10.1016\/j.ortho.2022.100691","volume":"20","author":"B Ristau","year":"2022","unstructured":"Ristau B, Coreil M, Chapple A, Armbruster P, Ballard R (2022) Comparison of AudaxCeph\u00ae\u2019s fully automated cephalometric tracing technology to a semi-automated approach by human examiners. Int Orthod 20(4):100691. https:\/\/doi.org\/10.1016\/j.ortho.2022.100691","journal-title":"Int Orthod"},{"key":"77_CR228","unstructured":"Russell SJ, Norvig P (2021) Artificial Intelligence: A Modern Approach, Global Edition 4e"},{"issue":"1","key":"77_CR229","doi-asserted-by":"publisher","DOI":"10.1186\/s12903-022-02466-x","volume":"22","author":"J Ryu","year":"2022","unstructured":"Ryu J, Lee YS, Mo SP, Lim K, Jung SK, Kim TW (2022) Application of deep learning artificial intelligence technique to the classification of clinical orthodontic photos. BMC Oral Health 22(1):454. https:\/\/doi.org\/10.1186\/s12903-022-02466-x","journal-title":"BMC Oral Health"},{"issue":"1","key":"77_CR230","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-32514-7","volume":"13","author":"J Ryu","year":"2023","unstructured":"Ryu J, Kim YH, Kim TW, Jung SK (2023) Evaluation of artificial intelligence model for crowding categorization and extraction diagnosis using intraoral photographs. Sci Rep 13(1):5177. https:\/\/doi.org\/10.1038\/s41598-023-32514-7","journal-title":"Sci Rep"},{"issue":"6","key":"77_CR231","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0305947","volume":"19","author":"J Sahlsten","year":"2024","unstructured":"Sahlsten J, J\u00e4rnstedt J, Jaskari J, Naukkarinen H, Mahasantipiya P, Charuakkra A, Vasankari K, Hietanen A, Sundqvist O, Lehtinen A, Kaski K (2024) Deep learning for 3D cephalometric landmarking with heterogeneous multi-center CBCT dataset. PLoS ONE 19(6):e0305947. https:\/\/doi.org\/10.1371\/journal.pone.0305947","journal-title":"PLoS ONE"},{"key":"77_CR232","doi-asserted-by":"publisher","unstructured":"Saifeldin H, Osorio J, Xi M, Safwat B, Khokher MR, Li S, . . . Wang D (2024) Accuracy of eyes of aitm artificial intelligence-driven platform for lateral cephalometric analysis. Ain Shams Dent J (Egypt) 33(1):1\u201310. https:\/\/doi.org\/10.21608\/asdj.2024.277176.1229","DOI":"10.21608\/asdj.2024.277176.1229"},{"issue":"4","key":"77_CR233","doi-asserted-by":"publisher","first-page":"255","DOI":"10.25259\/APOS_221_2023","volume":"14","author":"F Salmanpour","year":"2024","unstructured":"Salmanpour F, Camci H (2024) Artificial intelligence for predicting orthodontic patient cooperation: voice records versus frontal photographs. APOS Trends Orthod 14(4):255\u2013263. https:\/\/doi.org\/10.25259\/APOS_221_2023","journal-title":"APOS Trends Orthod"},{"issue":"1","key":"77_CR234","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1111\/ocr.12584","volume":"26","author":"S \u015eatir","year":"2023","unstructured":"\u015eatir S, B\u00fcy\u00fck\u00e7avu\u015f MH, Sari \u00d6, \u00c7imen T (2023) A novel approach to radiographic detection of growth development period with hand-wrist radiographs: a preliminary study with ImageJ imaging software. Orthod Craniofac Res 26(1):100\u2013106. https:\/\/doi.org\/10.1111\/ocr.12584","journal-title":"Orthod Craniofac Res"},{"issue":"5","key":"77_CR235","doi-asserted-by":"publisher","first-page":"1723","DOI":"10.3233\/thc-220563","volume":"31","author":"GB Senirkentli","year":"2023","unstructured":"Senirkentli GB, \u0130nce Bing\u00f6l S, \u00dcnal M, Bostanc\u0131 E, G\u00fczel MS, A\u00e7\u0131c\u0131 K (2023) Machine learning based orthodontic treatment planning for mixed dentition borderline cases suffering from moderate to severe crowding: an experimental research study. Technol Health Care 31(5):1723\u20131735. https:\/\/doi.org\/10.3233\/thc-220563","journal-title":"Technol Health Care"},{"key":"77_CR236","doi-asserted-by":"publisher","DOI":"10.3390\/jcm10163591","author":"H Seo","year":"2021","unstructured":"Seo H, Hwang J, Jeong T, Shin J (2021) Comparison of deep learning models for cervical vertebral maturation stage classification on lateral cephalometric radiographs. J Clin Med. https:\/\/doi.org\/10.3390\/jcm10163591","journal-title":"J Clin Med"},{"issue":"1","key":"77_CR237","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1016\/j.jds.2022.07.018","volume":"18","author":"H Seo","year":"2023","unstructured":"Seo H, Hwang J, Jung Y-H, Lee E, Nam OH, Shin J (2023) Deep focus approach for accurate bone age estimation from lateral cephalogram. J Dent Sci 18(1):34\u201343. https:\/\/doi.org\/10.1016\/j.jds.2022.07.018","journal-title":"J Dent Sci"},{"issue":"4","key":"77_CR238","doi-asserted-by":"publisher","first-page":"2568","DOI":"10.1177\/1460458220911789","volume":"26","author":"N Shafi","year":"2020","unstructured":"Shafi N, Bukhari F, Iqbal W, Almustafa KM, Asif M, Nawaz Z (2020) Cleft prediction before birth using deep neural network. Health Informatics J 26(4):2568\u20132585. https:\/\/doi.org\/10.1177\/1460458220911789","journal-title":"Health Informatics J"},{"issue":"3","key":"77_CR239","doi-asserted-by":"publisher","first-page":"232","DOI":"10.1177\/0022034520969115","volume":"100","author":"T Shan","year":"2021","unstructured":"Shan T, Tay FR, Gu L (2021) Application of artificial intelligence in dentistry. J Dent Res 100(3):232\u2013244. https:\/\/doi.org\/10.1177\/0022034520969115","journal-title":"J Dent Res"},{"issue":"4","key":"77_CR240","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1093\/ejo\/cjab083","volume":"44","author":"Y Shimizu","year":"2022","unstructured":"Shimizu Y, Tanikawa C, Kajiwara T, Nagahara H, Yamashiro T (2022) The validation of orthodontic artificial intelligence systems that perform orthodontic diagnoses and treatment planning. Eur J Orthod 44(4):436\u2013444. https:\/\/doi.org\/10.1093\/ejo\/cjab083","journal-title":"Eur J Orthod"},{"issue":"1","key":"77_CR241","doi-asserted-by":"publisher","DOI":"10.1186\/s40510-024-00527-1","volume":"25","author":"SA Shoari","year":"2024","unstructured":"Shoari SA, Sadrolashrafi SV, Sohrabi A, Afrouzian R, Ebrahimi P, Kouhsoltani M, Soltani MK (2024) Estimating mandibular growth stage based on cervical vertebral maturation in lateral cephalometric radiographs using artificial intelligence. Prog Orthod 25(1):28. https:\/\/doi.org\/10.1186\/s40510-024-00527-1","journal-title":"Prog Orthod"},{"key":"77_CR242","doi-asserted-by":"crossref","unstructured":"Shojaei H, Augusto V (2022) 'Constructing machine learning models for orthodontic treatment planning: a comparison of different methods'. Paper presented at the Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022","DOI":"10.1109\/BigData55660.2022.10021045"},{"key":"77_CR243","doi-asserted-by":"publisher","DOI":"10.1259\/DMFR.20200548","author":"TP Silva","year":"2022","unstructured":"Silva TP, Hughes MM, dos Santos Menezes L, de Melo MDFB, de Freitas PHL, Takeshita WM (2022) Artificial intelligence-based cephalometric landmark annotation and measurements according to Arnett\u2019s analysis: can we trust a bot to do that? Dentomaxillofac Radiol. https:\/\/doi.org\/10.1259\/DMFR.20200548","journal-title":"Dentomaxillofac Radiol"},{"issue":"Suppl 1","key":"77_CR244","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1111\/ocr.12666","volume":"26","author":"V Snider","year":"2023","unstructured":"Snider V, Homsi K, Kusnoto B, Atsawasuwan P, Viana G, Allareddy V, Gajendrareddy P, Elnagar MH (2023) Effectiveness of AI-driven remote monitoring technology in improving oral hygiene during orthodontic treatment. Orthod Craniofac Res 26(Suppl 1):102\u2013110. https:\/\/doi.org\/10.1111\/ocr.12666","journal-title":"Orthod Craniofac Res"},{"issue":"2","key":"77_CR245","first-page":"70","volume":"12","author":"M Soleiman Mezerji","year":"2023","unstructured":"Soleiman Mezerji M, Sheikhzadeh S, Mirzaie M, Gholinia H (2023) Fully automated orthodontic photograph analysis by machine learning. Caspian J Dent Res 12(2):70\u201381","journal-title":"Caspian J Dent Res"},{"issue":"7","key":"77_CR246","doi-asserted-by":"publisher","DOI":"10.3390\/app10072547","volume":"10","author":"Y Song","year":"2020","unstructured":"Song Y, Qiao X, Iwamoto Y, Chen Y-W (2020) Automatic cephalometric landmark detection on X-ray images using a deep-learning method. Appl Sci 10(7):2547","journal-title":"Appl Sci"},{"key":"77_CR247","doi-asserted-by":"publisher","DOI":"10.3390\/bioengineering11090861","author":"L Stetzel","year":"2024","unstructured":"Stetzel L, Foucher F, Jang SJ, Wu TH, Fields H, Schumacher F, Richmond S, Ko CC (2024) Artificial intelligence for predicting the aesthetic component of the index of orthodontic treatment need. Bioengineering. https:\/\/doi.org\/10.3390\/bioengineering11090861","journal-title":"Bioengineering"},{"key":"77_CR248","doi-asserted-by":"crossref","unstructured":"Strunga, M., Ballova, D. S., Tomasik, J., Oravcova, L., Danisovic, L., & Thurzo, A. (2024). AI-automated Cephalometric Tracing: A New Normal in Orthodontics? Paper presented at the International Conference on Artificial Intelligence, Computer, Data Sciences, and Applications, ACDSA 2024.","DOI":"10.1109\/ACDSA59508.2024.10467825"},{"issue":"2","key":"77_CR249","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2024.e24097","volume":"10","author":"S Su","year":"2024","unstructured":"Su S, Jia X, Zhan L, Gao S, Zhang Q, Huang X (2024) Automatic tooth periodontal ligament segmentation of cone beam computed tomography based on instance segmentation network. Heliyon 10(2):e24097. https:\/\/doi.org\/10.1016\/j.heliyon.2024.e24097","journal-title":"Heliyon"},{"key":"77_CR250","doi-asserted-by":"crossref","unstructured":"Tafala I, Bourzgui F, Othmani MB, Azmi M (2022) Automatic classification of malocclusion. Paper presented at the Procedia Computer Science","DOI":"10.1016\/j.procs.2022.10.154"},{"issue":"3","key":"77_CR251","doi-asserted-by":"publisher","first-page":"190","DOI":"10.1016\/j.jebdp.2016.07.002","volume":"16","author":"K Takada","year":"2016","unstructured":"Takada K (2016) Artificial intelligence expert systems with neural network machine learning may assist decision-making for extractions in orthodontic treatment planning. J Evid Based Dent Pract 16(3):190\u2013192. https:\/\/doi.org\/10.1016\/j.jebdp.2016.07.002","journal-title":"J Evid Based Dent Pract"},{"issue":"3","key":"77_CR252","doi-asserted-by":"publisher","first-page":"957","DOI":"10.1016\/j.jds.2020.10.012","volume":"16","author":"S Takeda","year":"2021","unstructured":"Takeda S, Mine Y, Yoshimi Y, Ito S, Tanimoto K, Murayama T (2021) Landmark annotation and mandibular lateral deviation analysis of posteroanterior cephalograms using a convolutional neural network. J Dent Sci 16(3):957\u2013963. https:\/\/doi.org\/10.1016\/j.jds.2020.10.012","journal-title":"J Dent Sci"},{"issue":"2","key":"77_CR253","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1053\/j.sodo.2021.05.012","volume":"27","author":"S Talaat","year":"2021","unstructured":"Talaat S, Kaboudan A, Talaat W, Kusnoto B, Sanchez F, Elnagar MH, Bourauel C, Ghoneima A (2021) The validity of an artificial intelligence application for assessment of orthodontic treatment need from clinical images. Semin Orthod 27(2):164\u2013171. https:\/\/doi.org\/10.1053\/j.sodo.2021.05.012","journal-title":"Semin Orthod"},{"key":"77_CR254","doi-asserted-by":"publisher","DOI":"10.3390\/app14062567","author":"JD Tamayo-Quintero","year":"2024","unstructured":"Tamayo-Quintero JD, G\u00f3mez-Mendoza JB, Guevara-P\u00e9rez SV (2024) DentalArch: AI-based arch shape detection in orthodontics. Appl Sci. https:\/\/doi.org\/10.3390\/app14062567","journal-title":"Appl Sci"},{"issue":"5","key":"77_CR255","doi-asserted-by":"publisher","DOI":"10.1590\/2177-6709.28.5.e2323183.oar","volume":"28","author":"OM Tanaka","year":"2023","unstructured":"Tanaka OM, Gasparello GG, Hartmann GC, Casagrande FA, Pithon MM (2023) Assessing the reliability of ChatGPT: a content analysis of self-generated and self-answered questions on clear aligners, TADs and digital imaging. Dent Press J Orthod 28(5):e2323183. https:\/\/doi.org\/10.1590\/2177-6709.28.5.e2323183.oar","journal-title":"Dent Press J Orthod"},{"issue":"1","key":"77_CR256","doi-asserted-by":"publisher","DOI":"10.1186\/s12911-024-02598-w","volume":"24","author":"H Tang","year":"2024","unstructured":"Tang H, Liu S, Tan W, Fu L, Yan M, Feng H (2024) Prediction of midpalatal suture maturation stage based on transfer learning and enhanced vision transformer. BMC Med Inform Decis Mak 24(1):232. https:\/\/doi.org\/10.1186\/s12911-024-02598-w","journal-title":"BMC Med Inform Decis Mak"},{"issue":"Suppl 2","key":"77_CR257","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1111\/ocr.12501","volume":"24","author":"C Tanikawa","year":"2021","unstructured":"Tanikawa C, Lee C, Lim J, Oka A, Yamashiro T (2021a) Clinical applicability of automated cephalometric landmark identification: Part I-patient-related identification errors. Orthod Craniofac Res 24(Suppl 2):43\u201352. https:\/\/doi.org\/10.1111\/ocr.12501","journal-title":"Orthod Craniofac Res"},{"issue":"S2","key":"77_CR258","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1111\/ocr.12501","volume":"24","author":"C Tanikawa","year":"2021","unstructured":"Tanikawa C, Lee C, Lim J, Oka A, Yamashiro T (2021b) Clinical applicability of automated cephalometric landmark identification: part I\u2014patient-related identification errors. Orthod Craniofac Res 24(S2):43\u201352. https:\/\/doi.org\/10.1111\/ocr.12501","journal-title":"Orthod Craniofac Res"},{"issue":"1","key":"77_CR259","doi-asserted-by":"publisher","DOI":"10.1186\/s12903-024-04512-2","volume":"24","author":"C Tanikawa","year":"2024","unstructured":"Tanikawa C, Tan TJ, Takada K (2024) Facial soft-tissue shape changes after fixed edgewise treatment with premolar extraction in individual artificial-intelligence-classified facial profile patterns. BMC Oral Health 24(1):740. https:\/\/doi.org\/10.1186\/s12903-024-04512-2","journal-title":"BMC Oral Health"},{"key":"77_CR260","doi-asserted-by":"crossref","unstructured":"Tanikawa C, Chonho L (2021) Machine learning for facial recognition in orthodontics. In Machine Learning in Dentistry, pp 55\u201365","DOI":"10.1007\/978-3-030-71881-7_5"},{"key":"77_CR261","doi-asserted-by":"publisher","DOI":"10.1016\/j.jdent.2023.104582","author":"B Tao","year":"2023","unstructured":"Tao B, Yu X, Wang W, Wang H, Chen X, Wang F, Wu Y (2023a) A deep learning-based automatic segmentation of zygomatic bones from cone-beam computed tomography images: a proof of concept. J Dent. https:\/\/doi.org\/10.1016\/j.jdent.2023.104582","journal-title":"J Dent"},{"issue":"1","key":"77_CR262","doi-asserted-by":"publisher","DOI":"10.1186\/s12903-023-03446-5","volume":"23","author":"L Tao","year":"2023","unstructured":"Tao L, Li M, Zhang X, Cheng M, Yang Y, Fu Y, Zhang R, Qian D, Yu H (2023b) Automatic craniomaxillofacial landmarks detection in CT images of individuals with dentomaxillofacial deformities by a two-stage deep learning model. BMC Oral Health 23(1):876. https:\/\/doi.org\/10.1186\/s12903-023-03446-5","journal-title":"BMC Oral Health"},{"issue":"11","key":"77_CR263","doi-asserted-by":"publisher","first-page":"1391","DOI":"10.1016\/j.joms.2023.07.137","volume":"81","author":"S Taraji","year":"2023","unstructured":"Taraji S, Atici SF, Viana G, Kusnoto B, Allareddy V, Miloro M, Elnagar MH (2023) Novel machine learning algorithms for prediction of treatment decisions in adult patients with class III malocclusion. J Oral Maxillofac Surg 81(11):1391\u20131402. https:\/\/doi.org\/10.1016\/j.joms.2023.07.137","journal-title":"J Oral Maxillofac Surg"},{"issue":"1","key":"77_CR264","doi-asserted-by":"publisher","first-page":"22","DOI":"10.4258\/hir.2018.24.1.22","volume":"24","author":"B Thanathornwong","year":"2018","unstructured":"Thanathornwong B (2018) Bayesian-based decision support system for assessing the needs for orthodontic treatment. Healthc Inform Res 24(1):22\u201328. https:\/\/doi.org\/10.4258\/hir.2018.24.1.22","journal-title":"Healthc Inform Res"},{"key":"77_CR265","doi-asserted-by":"publisher","DOI":"10.3390\/healthcare9121695","author":"A Thurzo","year":"2021","unstructured":"Thurzo A, Kurilov\u00e1 V, Varga I (2021) Artificial intelligence in orthodontic smart application for treatment coaching and its impact on clinical performance of patients monitored with AI-telehealth system. Healthcare. https:\/\/doi.org\/10.3390\/healthcare9121695","journal-title":"Healthcare"},{"key":"77_CR266","doi-asserted-by":"crossref","unstructured":"Tian JL, Zhang QY, Li HZ, Wang Q, Lei Y, Zang L, . . . Yang JJ (2022) Study of facial generation methods after orthodontic treatment. Paper presented at the Proceedings - 2022 IEEE 46th Annual Computers, Software, and Applications Conference, COMPSAC 2022","DOI":"10.1109\/COMPSAC54236.2022.00156"},{"key":"77_CR267","doi-asserted-by":"publisher","DOI":"10.3390\/app14177837","author":"J Tom\u00e1\u0161ik","year":"2024","unstructured":"Tom\u00e1\u0161ik J, Zsoldos M, Majd\u00e1kov\u00e1 K, Fleischmann A, Oravcov\u00e1 \u013d, S\u00f3nak Ballov\u00e1 D, Thurzo A (2024) The potential of AI-powered face enhancement technologies in face-driven orthodontic treatment planning. Appl Sci. https:\/\/doi.org\/10.3390\/app14177837","journal-title":"Appl Sci"},{"issue":"4","key":"77_CR268","doi-asserted-by":"publisher","first-page":"281","DOI":"10.18231\/j.jco.2023.048","volume":"7","author":"M Trehan","year":"2023","unstructured":"Trehan M, Bhanotia D, Shaikh TA, Sharma S, Sharma S (2023) Artificial intelligence-based automated model for prediction of extraction using neural network machine learning: a scope and performance analysis. J Contemp Orthod 7(4):281\u2013286. https:\/\/doi.org\/10.18231\/j.jco.2023.048","journal-title":"J Contemp Orthod"},{"key":"77_CR269","doi-asserted-by":"publisher","DOI":"10.3390\/jcm11226854","author":"IA Tsolakis","year":"2022","unstructured":"Tsolakis IA, Tsolakis AI, Elshebiny T, Matthaios S, Palomo JM (2022) Comparing a fully automated cephalometric tracing method to a manual tracing method for orthodontic diagnosis. J Clin Med. https:\/\/doi.org\/10.3390\/jcm11226854","journal-title":"J Clin Med"},{"key":"77_CR270","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics13132134","author":"A Ueda","year":"2023","unstructured":"Ueda A, Tussie C, Kim S, Kuwajima Y, Matsumoto S, Kim G, Nagai S (2023) Classification of maxillofacial morphology by artificial intelligence using cephalometric analysis measurements. Diagnostics. https:\/\/doi.org\/10.3390\/diagnostics13132134","journal-title":"Diagnostics"},{"issue":"2","key":"77_CR271","doi-asserted-by":"publisher","first-page":"94","DOI":"10.5152\/TurkJOrthod.2022.22026","volume":"35","author":"M Ugurlu","year":"2022","unstructured":"Ugurlu M (2022) Performance of a convolutional neural network-based artificial intelligence algorithm for automatic cephalometric landmark detection. Turk J Orthod 35(2):94\u2013100. https:\/\/doi.org\/10.5152\/TurkJOrthod.2022.22026","journal-title":"Turk J Orthod"},{"key":"77_CR272","doi-asserted-by":"publisher","DOI":"10.1016\/j.jdent.2021.103786","volume":"114","author":"PJ Verhelst","year":"2021","unstructured":"Verhelst PJ, Smolders A, Beznik T, Meewis J, Vandemeulebroucke A, Shaheen E, Jacobs R (2021) Layered deep learning for automatic mandibular segmentation in cone-beam computed tomography. J Dent 114:103786. https:\/\/doi.org\/10.1016\/j.jdent.2021.103786","journal-title":"J Dent"},{"key":"77_CR273","doi-asserted-by":"publisher","DOI":"10.1016\/j.jdent.2023.104475","author":"S Vinayahalingam","year":"2023","unstructured":"Vinayahalingam S, Berends B, Baan F, Moin DA, van Luijn R, Berg\u00e9 S, Xi T (2023a) Deep learning for automated segmentation of the temporomandibular joint. J Dent. https:\/\/doi.org\/10.1016\/j.jdent.2023.104475","journal-title":"J Dent"},{"key":"77_CR274","doi-asserted-by":"publisher","DOI":"10.1186\/s12903-023-03362-8","author":"S Vinayahalingam","year":"2023","unstructured":"Vinayahalingam S, Kempers S, Schoep J, Hsu TMH, Moin DA, van Ginneken B, Xi T (2023b) Intra-oral scan segmentation using deep learning. BMC Oral Health. https:\/\/doi.org\/10.1186\/s12903-023-03362-8","journal-title":"BMC Oral Health"},{"key":"77_CR275","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics13172740","author":"J Volovic","year":"2023","unstructured":"Volovic J, Badirli S, Ahmad S, Leavitt L, Mason T, Bhamidipalli SS, Eckert G, Albright D, Turkkahraman H (2023) A novel machine learning model for predicting orthodontic treatment duration. Diagnostics. https:\/\/doi.org\/10.3390\/diagnostics13172740","journal-title":"Diagnostics"},{"issue":"9","key":"77_CR276","doi-asserted-by":"publisher","first-page":"943","DOI":"10.1177\/00220345211005338","volume":"100","author":"H Wang","year":"2021","unstructured":"Wang H, Minnema J, Batenburg KJ, Forouzanfar T, Hu FJ, Wu G (2021a) Multiclass CBCT image segmentation for orthodontics with deep learning. J Dent Res 100(9):943\u2013949. https:\/\/doi.org\/10.1177\/00220345211005338","journal-title":"J Dent Res"},{"key":"77_CR277","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1111\/ocr.12482","volume":"24","author":"X Wang","year":"2021","unstructured":"Wang X, Pastewait M, Wu TH, Lian C, Tejera B, Lee YT, Ko CC (2021b) 3D morphometric quantification of maxillae and defects for patients with unilateral cleft palate via deep learning-based CBCT image auto-segmentation. Orthod Craniofac Res 24:108\u2013116. https:\/\/doi.org\/10.1111\/ocr.12482","journal-title":"Orthod Craniofac Res"},{"issue":"1","key":"77_CR278","doi-asserted-by":"publisher","DOI":"10.1186\/s12903-024-04752-2","volume":"24","author":"T Wang","year":"2024","unstructured":"Wang T, Nie K, Fan Y, Chen G, Xu K, Han B, Pei Y, Song G, Xu T (2024b) Network analysis of three-dimensional hard-soft tissue relationships in the lower 1\/3 of the face: skeletal class I-normodivergent malocclusion versus class II-hyperdivergent malocclusion. BMC Oral Health 24(1):996. https:\/\/doi.org\/10.1186\/s12903-024-04752-2","journal-title":"BMC Oral Health"},{"issue":"1","key":"77_CR279","doi-asserted-by":"publisher","DOI":"10.1186\/s12903-024-04582-2","volume":"24","author":"X Wang","year":"2024","unstructured":"Wang X, Alqahtani KA, Van den Bogaert T, Shujaat S, Jacobs R, Shaheen E (2024c) Convolutional neural network for automated tooth segmentation on intraoral scans. BMC Oral Health 24(1):804. https:\/\/doi.org\/10.1186\/s12903-024-04582-2","journal-title":"BMC Oral Health"},{"key":"77_CR280","doi-asserted-by":"publisher","DOI":"10.1186\/s40001-024-01681-2","author":"Y Wang","year":"2024","unstructured":"Wang Y, Wu W, Christelle M, Sun M, Wen Z, Lin Y, Zhang H, Xu J (2024d) Automated localization of mandibular landmarks in the construction of mandibular median sagittal plane. Eur J Med Res. https:\/\/doi.org\/10.1186\/s40001-024-01681-2","journal-title":"Eur J Med Res"},{"key":"77_CR281","doi-asserted-by":"crossref","unstructured":"Wang HY, Li KH, Zhu JH, Wang F, Lian CF, Ma JH (2024, Oct 06\u201310) Weakly supervised tooth instance segmentation on 3D dental models with multi-label learning. Paper presented at the 27th international conference on medical image computing and computer assisted intervention (MICCAI), Palmeraie Conf Ctr, Marrakesh, Morocco","DOI":"10.1007\/978-3-031-72114-4_69"},{"key":"77_CR282","doi-asserted-by":"publisher","DOI":"10.1016\/j.cagd.2022.102077","author":"G Wei","year":"2022","unstructured":"Wei G, Cui Z, Zhu J, Yang L, Zhou Y, Singh P, Gu M, Wang W (2022) Dense representative tooth landmark\/axis detection network on 3D model. Comput Aided Geom des. https:\/\/doi.org\/10.1016\/j.cagd.2022.102077","journal-title":"Comput Aided Geom des"},{"issue":"2","key":"77_CR283","doi-asserted-by":"publisher","first-page":"536","DOI":"10.1177\/0033294118763172","volume":"122","author":"RS Westfall","year":"2019","unstructured":"Westfall RS, Millar MG, Lovitt A (2019) The influence of physical attractiveness on belief in a just world. Psychol Rep 122(2):536\u2013549. https:\/\/doi.org\/10.1177\/0033294118763172","journal-title":"Psychol Rep"},{"issue":"3","key":"77_CR284","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1515\/cdbme-2020-3015","volume":"6","author":"A Wirtz","year":"2020","unstructured":"Wirtz A, Lam J, Wesarg S (2020) Automated cephalometric landmark localization using a coupled shape model. Curr Dir Biomed Eng 6(3):56\u201359. https:\/\/doi.org\/10.1515\/cdbme-2020-3015","journal-title":"Curr Dir Biomed Eng"},{"key":"77_CR285","doi-asserted-by":"publisher","DOI":"10.3390\/jcm13133672","author":"D Wolf","year":"2024","unstructured":"Wolf D, Farrag G, Fl\u00fcgge T, Timm LH (2024) Predicting outcome in clear aligner treatment: a machine learning analysis. J Clin Med. https:\/\/doi.org\/10.3390\/jcm13133672","journal-title":"J Clin Med"},{"key":"77_CR286","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics13091553","author":"T Wood","year":"2023","unstructured":"Wood T, Anigbo JO, Eckert G, Stewart KT, Dundar MM, Turkkahraman H (2023) Prediction of the post-pubertal mandibular length and Y axis of growth by using various machine learning techniques: a retrospective longitudinal study. Diagnostics. https:\/\/doi.org\/10.3390\/diagnostics13091553","journal-title":"Diagnostics"},{"issue":"11","key":"77_CR287","doi-asserted-by":"publisher","first-page":"3158","DOI":"10.1109\/tmi.2022.3180343","volume":"41","author":"TH Wu","year":"2022","unstructured":"Wu TH, Lian C, Lee S, Pastewait M, Piers C, Liu J, Wang F, Wang Li, Chiu C-Y, Wang W, Jackson C, Chao W-L, Shen D, Ko CC (2022) Two-stage mesh deep learning for automated tooth segmentation and landmark localization on 3D intraoral scans. IEEE Trans Med Imaging 41(11):3158\u20133166. https:\/\/doi.org\/10.1109\/tmi.2022.3180343","journal-title":"IEEE Trans Med Imaging"},{"issue":"2","key":"77_CR288","doi-asserted-by":"publisher","first-page":"262","DOI":"10.2319\/111608-588.1","volume":"80","author":"X Xie","year":"2010","unstructured":"Xie X, Wang L, Wang A (2010) Artificial neural network modeling for deciding if extractions are necessary prior to orthodontic treatment. Angle Orthod 80(2):262\u2013266. https:\/\/doi.org\/10.2319\/111608-588.1","journal-title":"Angle Orthod"},{"issue":"3","key":"77_CR289","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1016\/j.ajodo.2023.01.017","volume":"164","author":"L Xing","year":"2023","unstructured":"Xing L, Zhang X, Guo Y, Bai D, Xu H (2023) XGBoost-aided prediction of lip prominence based on hard-tissue measurements and demographic characteristics in an Asian population. Am J Orthod Dentofacial Orthop 164(3):357\u2013367. https:\/\/doi.org\/10.1016\/j.ajodo.2023.01.017","journal-title":"Am J Orthod Dentofacial Orthop"},{"issue":"7","key":"77_CR290","doi-asserted-by":"publisher","first-page":"2336","DOI":"10.1109\/tvcg.2018.2839685","volume":"25","author":"X Xu","year":"2019","unstructured":"Xu X, Liu C, Zheng Y (2019) 3D tooth segmentation and labeling using deep convolutional neural networks. IEEE Trans vis Comput Graph 25(7):2336\u20132348. https:\/\/doi.org\/10.1109\/tvcg.2018.2839685","journal-title":"IEEE Trans vis Comput Graph"},{"issue":"4","key":"77_CR291","doi-asserted-by":"publisher","first-page":"268","DOI":"10.4041\/kjod21.255","volume":"52","author":"L Xu","year":"2022","unstructured":"Xu L, Mei L, Lu RQ, Li Y, Li HS, Li Y (2022) Predicting patient experience of Invisalign treatment: an analysis using artificial neural network. Korean J Orthod 52(4):268\u2013277. https:\/\/doi.org\/10.4041\/kjod21.255","journal-title":"Korean J Orthod"},{"key":"77_CR292","doi-asserted-by":"publisher","DOI":"10.1007\/s10278-024-01045-6","author":"S Xu","year":"2024","unstructured":"Xu S, Peng H, Yang L, Zhong W, Gao X, Song J (2024) An automatic grading system for orthodontically induced external root resorption based on deep convolutional neural network. J Imaging Inform Med. https:\/\/doi.org\/10.1007\/s10278-024-01045-6","journal-title":"J Imaging Inform Med"},{"issue":"3","key":"77_CR293","doi-asserted-by":"publisher","first-page":"282","DOI":"10.1016\/j.ajodo.2024.05.015","volume":"166","author":"YM Yacout","year":"2024","unstructured":"Yacout YM, Eid FY, Tageldin MA, Kassem HE (2024) Evaluation of the accuracy of automated tooth segmentation of intraoral scans using artificial intelligence-based software packages. Am J Orthod Dentofacial Orthop 166(3):282-291.e281. https:\/\/doi.org\/10.1016\/j.ajodo.2024.05.015","journal-title":"Am J Orthod Dentofacial Orthop"},{"issue":"1","key":"77_CR294","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1016\/S0915-6992(03)80029-8","volume":"15","author":"G Yamamoto","year":"2003","unstructured":"Yamamoto G, Ohta Y, Tsuda Y, Tanaka A, Nishikawa M, Inoda H (2003) A new classification of impacted canines and second premolars using orthopantomography. Asian J Oral Maxillofac Surg 15(1):31\u201337. https:\/\/doi.org\/10.1016\/S0915-6992(03)80029-8","journal-title":"Asian J Oral Maxillofac Surg"},{"issue":"3","key":"77_CR295","doi-asserted-by":"publisher","first-page":"e250","DOI":"10.1016\/j.ajodo.2021.09.012","volume":"161","author":"J Yao","year":"2022","unstructured":"Yao J, Zeng W, He T, Zhou S, Zhang Y, Guo J, Tang W (2022) Automatic localization of cephalometric landmarks based on convolutional neural network. Am J Orthod Dentofacial Orthop 161(3):e250\u2013e259. https:\/\/doi.org\/10.1016\/j.ajodo.2021.09.012","journal-title":"Am J Orthod Dentofacial Orthop"},{"key":"77_CR296","doi-asserted-by":"publisher","DOI":"10.1186\/s12903-023-03188-4","author":"H Ye","year":"2023","unstructured":"Ye H, Cheng Z, Ungvijanpunya N, Chen W, Cao L, Gou Y (2023) Is automatic cephalometric software using artificial intelligence better than orthodontist experts in landmark identification? BMC Oral Health. https:\/\/doi.org\/10.1186\/s12903-023-03188-4","journal-title":"BMC Oral Health"},{"issue":"3","key":"77_CR297","doi-asserted-by":"publisher","first-page":"410","DOI":"10.2319\/071513-516.1","volume":"84","author":"X Yu","year":"2014","unstructured":"Yu X, Liu B, Pei Y, Xu T (2014) Evaluation of facial attractiveness for patients with malocclusion: a machine-learning technique employing procrustes. Angle Orthod 84(3):410\u2013416. https:\/\/doi.org\/10.2319\/071513-516.1","journal-title":"Angle Orthod"},{"issue":"3","key":"77_CR298","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1177\/0022034520901715","volume":"99","author":"HJ Yu","year":"2020","unstructured":"Yu HJ, Cho SR, Kim MJ, Kim WH, Kim JW, Choi J (2020) Automated skeletal classification with lateral cephalometry based on artificial intelligence. J Dent Res 99(3):249\u2013256. https:\/\/doi.org\/10.1177\/0022034520901715","journal-title":"J Dent Res"},{"issue":"6","key":"77_CR299","doi-asserted-by":"publisher","first-page":"712","DOI":"10.1093\/ejo\/cjad032","volume":"45","author":"JH Yu","year":"2023","unstructured":"Yu JH, Kim JH, Liu J, Mangal U, Ahn HK, Cha JY (2023) Reliability and time-based efficiency of artificial intelligence-based automatic digital model analysis system. Eur J Orthod 45(6):712\u2013721. https:\/\/doi.org\/10.1093\/ejo\/cjad032","journal-title":"Eur J Orthod"},{"issue":"2","key":"77_CR300","doi-asserted-by":"publisher","first-page":"218","DOI":"10.1002\/ca.24133","volume":"37","author":"S Yu","year":"2024","unstructured":"Yu S, Zheng Y, Dong L, Huang W, Wu H, Zhang Q, Yan X, Wu W, Lv T, Yuan X (2024) The accuracy and reliability of different midsagittal planes in the symmetry assessment using cone-beam computed tomography. Clin Anat 37(2):218\u2013226. https:\/\/doi.org\/10.1002\/ca.24133","journal-title":"Clin Anat"},{"key":"77_CR301","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1111\/ocr.12539","volume":"24","author":"E Yurdakurban","year":"2021","unstructured":"Yurdakurban E, Duran GS, G\u00f6rg\u00fcl\u00fc S (2021) Evaluation of an automated approach for facial midline detection and asymmetry assessment: a preliminary study. Orthod Craniofac Res 24:84\u201391. https:\/\/doi.org\/10.1111\/ocr.12539","journal-title":"Orthod Craniofac Res"},{"issue":"2","key":"77_CR302","doi-asserted-by":"publisher","first-page":"242","DOI":"10.1016\/j.ajodo.2024.09.006","volume":"167","author":"E Yurdakurban","year":"2025","unstructured":"Yurdakurban E, S\u00fck\u00fct Y, Duran GS (2025) Assessment of deep learning technique for fully automated mandibular segmentation. Am J Orthod Dentofacial Orthop 167(2):242\u2013249. https:\/\/doi.org\/10.1016\/j.ajodo.2024.09.006","journal-title":"Am J Orthod Dentofacial Orthop"},{"issue":"1","key":"77_CR303","doi-asserted-by":"publisher","DOI":"10.1186\/s12911-024-02664-3","volume":"24","author":"R Zaheer","year":"2024","unstructured":"Zaheer R, Shafique HZ, Khalid Z, Shahid R, Jan A, Zahoor T, Nawaz R, Ul Hassan M (2024) Comparison of semi and fully automated artificial intelligence driven softwares and manual system for cephalometric analysis. BMC Med Inform Decis Mak 24(1):271. https:\/\/doi.org\/10.1186\/s12911-024-02664-3","journal-title":"BMC Med Inform Decis Mak"},{"key":"77_CR304","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics13162713","author":"G Zakhar","year":"2023","unstructured":"Zakhar G, Hazime S, Eckert G, Wong A, Badirli S, Turkkahraman H (2023) Prediction of pubertal mandibular growth in males with class II malocclusion by utilizing machine learning. Diagnostics. https:\/\/doi.org\/10.3390\/diagnostics13162713","journal-title":"Diagnostics"},{"key":"77_CR305","doi-asserted-by":"publisher","DOI":"10.1016\/j.jdent.2024.105412","volume":"151","author":"PA Zecca","year":"2024","unstructured":"Zecca PA, Caccia M, Levrini L, Carganico A, Reguzzoni M, Donadio D, Tosi D, Protasoni M (2024) Ai-based open-source software for cephalometric analysis from limited FOV radiographs. J Dent 151:105412. https:\/\/doi.org\/10.1016\/j.jdent.2024.105412","journal-title":"J Dent"},{"key":"77_CR306","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101904","volume":"68","author":"M Zeng","year":"2021","unstructured":"Zeng M, Yan Z, Liu S, Zhou Y, Qiu L (2021) Cascaded convolutional networks for automatic cephalometric landmark detection. Med Image Anal 68:101904. https:\/\/doi.org\/10.1016\/j.media.2020.101904","journal-title":"Med Image Anal"},{"key":"77_CR307","unstructured":"Zese R, Lombardo L, De Maio M, Tamascelli M, Cremonini F (2023) A novel cephalometric tool enhanced by AI assistance. Paper presented at the CEUR Workshop Proceedings"},{"issue":"1","key":"77_CR308","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-022-00681-y","volume":"5","author":"Z Zhang","year":"2022","unstructured":"Zhang Z, Liu N, Guo Z, Jiao L, Fenster A, Jin W, Zhang Y, Chen J, Yan C, Gou S (2022) Ageing and degeneration analysis using ageing-related dynamic attention on lateral cephalometric radiographs. NPJ Digit Med 5(1):151. https:\/\/doi.org\/10.1038\/s41746-022-00681-y","journal-title":"NPJ Digit Med"},{"issue":"10","key":"77_CR309","doi-asserted-by":"publisher","first-page":"4926","DOI":"10.1109\/JBHI.2023.3297610","volume":"27","author":"D Zhang","year":"2023","unstructured":"Zhang D, Yang J, Du S, Bu W, Guo YC (2023) An uncertainty-aware and sex-prior guided biological age estimation from orthopantomogram images. IEEE J Biomed Health Inform 27(10):4926\u20134937. https:\/\/doi.org\/10.1109\/JBHI.2023.3297610","journal-title":"IEEE J Biomed Health Inform"},{"issue":"3","key":"77_CR310","doi-asserted-by":"publisher","DOI":"10.1177\/14604582241274511","volume":"30","author":"H Zhang","year":"2024","unstructured":"Zhang H, Liu C, Yang P, Yang S, Yu Q, Liu R (2024) The concept of AI-assisted self-monitoring for skeletal malocclusion. Health Informatics J 30(3):14604582241274511. https:\/\/doi.org\/10.1177\/14604582241274511","journal-title":"Health Informatics J"},{"key":"77_CR311","doi-asserted-by":"crossref","unstructured":"Zhang Y, Pei Y, Qin H, Guo Y, Ma G, Xu T, Zha H (2019, 8\u201311 April 2019) Masseter muscle segmentation from cone-beam CT images using generative adversarial network. Paper presented at the 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)","DOI":"10.1109\/ISBI.2019.8759426"},{"issue":"4","key":"77_CR312","doi-asserted-by":"publisher","first-page":"826","DOI":"10.1109\/tmi.2021.3124217","volume":"41","author":"Y Zhao","year":"2022","unstructured":"Zhao Y, Zhang L, Liu Y, Meng D, Cui Z, Gao C, Gao X, Lian C, Shen D (2022) Two-stream graph convolutional network for intra-oral scanner image segmentation. IEEE Trans Med Imaging 41(4):826\u2013835. https:\/\/doi.org\/10.1109\/tmi.2021.3124217","journal-title":"IEEE Trans Med Imaging"},{"key":"77_CR313","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2023.e17459","author":"CY Zhao","year":"2023","unstructured":"Zhao CY, Yuan ZB, Luo SC, Wang WJ, Ren Z, Yao XF, Wu T (2023a) Automatic recognition of cephalometric landmarks via multi-scale sampling strategy. Heliyon. https:\/\/doi.org\/10.1016\/j.heliyon.2023.e17459","journal-title":"Heliyon"},{"issue":"6","key":"77_CR314","doi-asserted-by":"publisher","first-page":"519","DOI":"10.3760\/cma.j.cn112144-20230218-00049","volume":"58","author":"Y Zhao","year":"2023","unstructured":"Zhao Y, Gao L, Wang Y (2023b) Advances in algorithms for three-dimensional craniomaxillofacial features construction based on point clouds. Chin J Stomatol 58(6):519\u2013526. https:\/\/doi.org\/10.3760\/cma.j.cn112144-20230218-00049","journal-title":"Chin J Stomatol"},{"issue":"7","key":"77_CR315","doi-asserted-by":"publisher","DOI":"10.3390\/children11070762","volume":"11","author":"L Zhao","year":"2024","unstructured":"Zhao L, Chen X, Huang J, Mo S, Gu M, Kang N, Song S, Zhang X, Liang B, Tang M (2024) Machine learning algorithms for the diagnosis of class III malocclusions in children. Children (Basel) 11(7):762. https:\/\/doi.org\/10.3390\/children11070762","journal-title":"Children (Basel)"},{"issue":"2","key":"77_CR316","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1016\/j.ajodo.2024.09.009","volume":"167","author":"Q Zheng","year":"2025","unstructured":"Zheng Q, Ma L, Wu Y, Gao Y, Li H, Lin J, Qing S, Long D, Chen X, Zhang W (2025) Automatic 3-dimensional quantification of orthodontically induced root resorption in cone-beam computed tomography images based on deep learning. Am J Orthod Dentofacial Orthop 167(2):188\u2013201. https:\/\/doi.org\/10.1016\/j.ajodo.2024.09.009","journal-title":"Am J Orthod Dentofacial Orthop"},{"key":"77_CR317","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics11122200","author":"J Zhou","year":"2021","unstructured":"Zhou J, Zhou H, Pu L, Gao Y, Tang Z, Yang Y, You M, Yang Z, Lai W, Long H (2021) Development of an artificial intelligence system for the automatic evaluation of cervical vertebral maturation status. Diagnostics. https:\/\/doi.org\/10.3390\/diagnostics11122200","journal-title":"Diagnostics"},{"key":"77_CR318","doi-asserted-by":"publisher","DOI":"10.1016\/j.jdent.2023.104808","volume":"141","author":"M Zhu","year":"2024","unstructured":"Zhu M, Yang P, Bian C, Zuo F, Guo Z, Wang Y, Bai Y, Zhang N (2024) Convolutional neural network-assisted diagnosis of midpalatal suture maturation stage in cone-beam computed tomography. J Dent 141:104808. https:\/\/doi.org\/10.1016\/j.jdent.2023.104808","journal-title":"J Dent"}],"container-title":["The Saudi Dental Journal"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44445-025-00077-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44445-025-00077-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44445-025-00077-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T14:03:35Z","timestamp":1763129015000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44445-025-00077-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10]]},"references-count":318,"journal-issue":{"issue":"7-9","published-print":{"date-parts":[[2025,10]]}},"alternative-id":["77"],"URL":"https:\/\/doi.org\/10.1007\/s44445-025-00077-0","relation":{},"ISSN":["1013-9052","2524-1702"],"issn-type":[{"value":"1013-9052","type":"print"},{"value":"2524-1702","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10]]},"assertion":[{"value":"7 June 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 October 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 October 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable. This study is a narrative review.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"Not applicable. This study is a narrative review.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"The authors declare no conflict of interest.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interest"}}],"article-number":"70"}}