{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T09:35:57Z","timestamp":1793352957723,"version":"4.5.0"},"reference-count":73,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T00:00:00Z","timestamp":1778630400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T00:00:00Z","timestamp":1778630400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Nigde Omer Halisdemir University","id":[{"id":"https:\/\/ror.org\/03ejnre35","id-type":"ROR","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Odontology"],"published-print":{"date-parts":[[2026,10]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>To systematically evaluate the diagnostic accuracy of artificial intelligence (AI) models in periapical radiography for detection, classification, and segmentation tasks compared to human experts, while critically appraising methodological quality and risk of bias. The systematic review was conducted in accordance with PRISMA guidelines and registered in PROSPERO (CRD420251132033). Strict exclusion criteria were applied to studies with insufficient dataset sizes (&lt;\u2009200 images) lacking augmentation, ambiguous reference standards, or reporting only accuracy without complementary metrics. A comprehensive literature search was conducted on June 23, 2025, across five electronic databases: PubMed\/MEDLINE, Scopus, ScienceDirect, Web of Science, and IEEE Xplore. The search strategy combined keywords related to \u201cperiapical radiography\u201d (e.g., periapical X-ray), \u201cartificial intelligence\u201d (e.g., deep learning, neural networks), and specific diagnostic tasks, with no restrictions on publication date or language. Studies utilizing artificial intelligence models for diagnostic tasks on periapical radiographs and validating performance against a human reference standard (expert consensus) were eligible for inclusion. Out of 544 identified records, 47 studies met the full eligibility criteria and were included in the qualitative synthesis. AI models possess a high diagnostic potential in periapical radiography, performing at a level comparable to human experts in tasks such as pathology detection, anatomical segmentation, and implant classification. However, their clinical applicability is currently limited by a high risk of bias, lack of external validation, reliance on cropped datasets, and the use of human consensus as a surrogate reference standard. Future research must prioritize full-arch evaluations, anatomical region-specific performance reporting, and adherence to AI-specific standardized metrics.<\/jats:p>","DOI":"10.1007\/s10266-026-01401-8","type":"journal-article","created":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T03:17:32Z","timestamp":1778642252000},"page":"1848-1864","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Diagnostic performance of artificial intelligence in periapical radiography: a systematic review"],"prefix":"10.1007","volume":"114","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4884-4715","authenticated-orcid":false,"given":"Tulin","family":"Tasdemir","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2062-5713","authenticated-orcid":false,"given":"Melek","family":"Tassoker","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-9653-4394","authenticated-orcid":false,"given":"Abdullah","family":"Arpac\u0131","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-9058-8359","authenticated-orcid":false,"given":"Ay\u015fenur","family":"Kula","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-0995-4609","authenticated-orcid":false,"given":"Ferhat Enes","family":"Kocabiyik","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-5145-2051","authenticated-orcid":false,"given":"Mehmet Enes","family":"\u0130yi\u015feny\u00fcrek","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5019-071X","authenticated-orcid":false,"given":"Huseyin","family":"Hakli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,13]]},"reference":[{"issue":"4","key":"1401_CR1","doi-asserted-by":"publisher","first-page":"2212","DOI":"10.1097\/ms9.0000000000003127","volume":"87","author":"M Ali","year":"2025","unstructured":"Ali M, Irfan M, Ali T, Wei CR, Akilimali A. Artificial intelligence in dental radiology: a narrative review. Ann Med Surg. 2025;87(4):2212\u20137. https:\/\/doi.org\/10.1097\/ms9.0000000000003127.","journal-title":"Ann Med Surg"},{"issue":"1","key":"1401_CR2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-025-33391-y","volume":"16","author":"L Wang","year":"2026","unstructured":"Wang L, Li Z. Transformer-based intelligent detection model for early dental caries in panoramic radiographs. Sci Rep. 2026;16(1):3507. https:\/\/doi.org\/10.1038\/s41598-025-33391-y.","journal-title":"Sci Rep"},{"issue":"1","key":"1401_CR3","doi-asserted-by":"publisher","first-page":"25429","DOI":"10.1038\/s41598-024-75748-9","volume":"14","author":"J Liu","year":"2024","unstructured":"Liu J, et al. Periapical lesion detection in periapical radiographs using the latest convolutional neural network ConvNeXt and its integrated models. Sci Rep. 2024;14(1):25429. https:\/\/doi.org\/10.1038\/s41598-024-75748-9.","journal-title":"Sci Rep"},{"key":"1401_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.jdent.2022.104107","volume":"122","author":"S Li","year":"2022","unstructured":"Li S, et al. Artificial intelligence for caries and periapical periodontitis detection. J Dent. 2022;122:104107. https:\/\/doi.org\/10.1016\/j.jdent.2022.104107.","journal-title":"J Dent"},{"issue":"4","key":"1401_CR5","doi-asserted-by":"publisher","first-page":"649","DOI":"10.1007\/s11548-021-02319-y","volume":"16","author":"H Chen","year":"2021","unstructured":"Chen H, Li H, Zhao Y, Zhao J, Wang Y. Dental disease detection on periapical radiographs based on deep convolutional neural networks. Int J Comput Assist Radiol Surg. 2021;16(4):649\u201361. https:\/\/doi.org\/10.1007\/s11548-021-02319-y.","journal-title":"Int J Comput Assist Radiol Surg"},{"issue":"11","key":"1401_CR6","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics15111432","volume":"15","author":"WI Ibraheem","year":"2025","unstructured":"Ibraheem WI, et al. Assessment of the diagnostic accuracy of artificial intelligence software in identifying common periodontal and restorative dental conditions (marginal bone loss, periapical lesion, crown, restoration, dental caries) in intraoral periapical radiographs. Diagnostics. 2025;15(11):1432. https:\/\/doi.org\/10.3390\/diagnostics15111432.","journal-title":"Diagnostics"},{"issue":"1","key":"1401_CR7","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-025-32226-0","volume":"16","author":"N Kazimierczak","year":"2025","unstructured":"Kazimierczak N, et al. Detection accuracy of an AI platform for dental treatment features on panoramic radiographs - tooth- and patient-level analyses. Sci Rep. 2025;16(1):2436. https:\/\/doi.org\/10.1038\/s41598-025-32226-0.","journal-title":"Sci Rep"},{"issue":"3","key":"1401_CR8","doi-asserted-by":"publisher","first-page":"248","DOI":"10.1016\/j.joen.2022.12.007","volume":"49","author":"S Sadr","year":"2023","unstructured":"Sadr S, et al. Deep learning for detection of periapical radiolucent lesions: a systematic review and meta-analysis of diagnostic test accuracy. J Endod. 2023;49(3):248-261.e3. https:\/\/doi.org\/10.1016\/j.joen.2022.12.007.","journal-title":"J Endod"},{"key":"1401_CR9","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.n71","volume":"372","author":"MJ Page","year":"2021","unstructured":"Page MJ, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71. https:\/\/doi.org\/10.1136\/bmj.n71.","journal-title":"BMJ"},{"issue":"1","key":"1401_CR10","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-021-00492-0","volume":"8","author":"C Shorten","year":"2021","unstructured":"Shorten C, Khoshgoftaar TM, Furht B. Text data augmentation for deep learning. J Big Data. 2021;8(1):101. https:\/\/doi.org\/10.1186\/s40537-021-00492-0.","journal-title":"J Big Data"},{"key":"1401_CR11","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1016\/j.jclinepi.2015.04.005","volume":"69","author":"EW Steyerberg","year":"2016","unstructured":"Steyerberg EW, Harrell FE Jr. Prediction models need appropriate internal, internal-external, and external validation. J Clin Epidemiol. 2016;69:245\u20137. https:\/\/doi.org\/10.1016\/j.jclinepi.2015.04.005.","journal-title":"J Clin Epidemiol"},{"issue":"Pt A","key":"1401_CR12","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1016\/j.neuroimage.2017.06.061","volume":"180","author":"G Varoquaux","year":"2018","unstructured":"Varoquaux G. Cross-validation failure: small sample sizes lead to large error bars. Neuroimage. 2018;180(Pt A):68\u201377. https:\/\/doi.org\/10.1016\/j.neuroimage.2017.06.061.","journal-title":"Neuroimage"},{"issue":"2","key":"1401_CR13","doi-asserted-by":"publisher","first-page":"486","DOI":"10.5812\/ijem.3505","volume":"10","author":"A Ghasemi","year":"2012","unstructured":"Ghasemi A, Zahediasl S. Normality tests for statistical analysis: a guide for non-statisticians. Int J Endocrinol Metab. 2012;10(2):486\u20139. https:\/\/doi.org\/10.5812\/ijem.3505.","journal-title":"Int J Endocrinol Metab"},{"issue":"1","key":"1401_CR14","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-019-0197-0","volume":"6","author":"C Shorten","year":"2019","unstructured":"Shorten C, Khoshgoftaar TM. A survey on image data augmentation for deep learning. J Big Data. 2019;6(1):60. https:\/\/doi.org\/10.1186\/s40537-019-0197-0.","journal-title":"J Big Data"},{"key":"1401_CR15","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.media.2017.07.005.","volume":"42","author":"G Litjens","year":"2017","unstructured":"Litjens G, et al. A survey on deep learning in medical image analysis. Med Image Anal. 2017;42:60\u201388. https:\/\/doi.org\/10.1016\/j.media.2017.07.005.","journal-title":"Med Image Anal"},{"issue":"1","key":"1401_CR16","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1148\/radiol.2020192224","volume":"295","author":"MJ Willemink","year":"2020","unstructured":"Willemink MJ, et al. Preparing medical imaging data for machine learning. Radiology. 2020;295(1):4\u201315. https:\/\/doi.org\/10.1148\/radiol.2020192224.","journal-title":"Radiology"},{"issue":"2","key":"1401_CR17","doi-asserted-by":"publisher","DOI":"10.1148\/ryai.2020200029","volume":"2","author":"J Mongan","year":"2020","unstructured":"Mongan J, Moy L, Kahn CE Jr. Checklist for artificial intelligence in medical imaging (CLAIM): a guide for authors and reviewers. Radiol Artif Intell. 2020;2(2):e200029. https:\/\/doi.org\/10.1148\/ryai.2020200029.","journal-title":"Radiol Artif Intell"},{"issue":"1","key":"1401_CR18","doi-asserted-by":"publisher","DOI":"10.1186\/s12864-019-6413-7","volume":"21","author":"D Chicco","year":"2020","unstructured":"Chicco D, Jurman G. The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genomics. 2020;21(1):6. https:\/\/doi.org\/10.1186\/s12864-019-6413-7.","journal-title":"BMC Genomics"},{"key":"1401_CR19","doi-asserted-by":"publisher","unstructured":"Baquhaizel AS, Boumeddane M, Gherram H, Alshaqaqi B. Enhancing Dental Caries Classification Through A VGG16-Based Transfer Learning, in 2023 International Conference on Electrical Engineering and Advanced Technology (ICEEAT), 5\u20137 Nov. 2023 2023, vol. 1, pp. 1\u20134. https:\/\/doi.org\/10.1109\/ICEEAT60471.2023.10426454","DOI":"10.1109\/ICEEAT60471.2023.10426454"},{"key":"1401_CR20","doi-asserted-by":"publisher","first-page":"18","DOI":"10.2340\/aos.v84.42599","volume":"84","author":"ZS Yavsan","year":"2025","unstructured":"Yavsan ZS, Orhan H, Efe E, Yavsan E. Diagnosis of approximal caries in children with convolutional neural networks based detection algorithms on radiographs: a pilot study. Acta Odontol Scand. 2025;84:18\u201325. https:\/\/doi.org\/10.2340\/aos.v84.42599.","journal-title":"Acta Odontol Scand"},{"issue":"1","key":"1401_CR21","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1007\/s11265-016-1214-6","volume":"90","author":"J Choi","year":"2018","unstructured":"Choi J, Eun H, Kim C. Boosting proximal dental caries detection via combination of variational methods and convolutional neural network. J Signal Process Syst. 2018;90(1):87\u201397. https:\/\/doi.org\/10.1007\/s11265-016-1214-6.","journal-title":"J Signal Process Syst"},{"issue":"9","key":"1401_CR22","doi-asserted-by":"publisher","first-page":"763","DOI":"10.21037\/atm-21-119","volume":"9","author":"L Zheng","year":"2021","unstructured":"Zheng L, Wang H, Mei L, Chen Q, Zhang Y, Zhang H. Artificial intelligence in digital cariology: a new tool for the diagnosis of deep caries and pulpitis using convolutional neural networks. Ann Transl Med. 2021;9(9):763. https:\/\/doi.org\/10.21037\/atm-21-119.","journal-title":"Ann Transl Med"},{"key":"1401_CR23","doi-asserted-by":"crossref","unstructured":"Zhu Y et al. Faster-RCNN based intelligent detection and localization of dental caries, Displays, vol. 74, p. 102201, 2022.","DOI":"10.1016\/j.displa.2022.102201"},{"issue":"1","key":"1401_CR24","doi-asserted-by":"publisher","first-page":"553","DOI":"10.1186\/s12903-023-03251-0","volume":"23","author":"A Altukroni","year":"2023","unstructured":"Altukroni A, et al. Detection of the pathological exposure of pulp using an artificial intelligence tool: a multicentric study over periapical radiographs. BMC Oral Health. 2023;23(1):553. https:\/\/doi.org\/10.1186\/s12903-023-03251-0.","journal-title":"BMC Oral Health"},{"issue":"3","key":"1401_CR25","doi-asserted-by":"publisher","first-page":"375","DOI":"10.1007\/s11282-024-00741-x","volume":"40","author":"Y Liu","year":"2024","unstructured":"Liu Y, Xia K, Cen Y, Ying S, Zhao Z. Artificial intelligence for caries detection: a novel diagnostic tool using deep learning algorithms. Oral Radiol. 2024;40(3):375\u201384. https:\/\/doi.org\/10.1007\/s11282-024-00741-x.","journal-title":"Oral Radiol"},{"issue":"3","key":"1401_CR26","doi-asserted-by":"publisher","DOI":"10.1002\/ima.70108","volume":"35","author":"SU Khan","year":"2025","unstructured":"Khan SU, Alanazi S, Almarshad F, Akram T. A novel approach for dental x-ray enhancement and caries detection. Int J Imaging Syst Technol. 2025;35(3):e70108.","journal-title":"Int J Imaging Syst Technol"},{"key":"1401_CR27","doi-asserted-by":"crossref","unstructured":"S PJUK. and K. S. R. S, Deep Fusion Model for Dental Caries Classification from X-Ray Images, 2024 10th International Conference on Advanced Computing and Communication Systems (ICACCS), vol. 1, pp. 1481\u20131485, 2024.","DOI":"10.1109\/ICACCS60874.2024.10717168"},{"key":"1401_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.jdent.2024.104970","volume":"144","author":"S Ying","year":"2024","unstructured":"Ying S, Huang F, Shen X, Liu W, He F. Performance comparison of multifarious deep networks on caries detection with tooth X-ray images. J Dent. 2024;144:104970. https:\/\/doi.org\/10.1016\/j.jdent.2024.104970.","journal-title":"J Dent"},{"key":"1401_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2024.107344","volume":"102","author":"J Liu","year":"2025","unstructured":"Liu J, et al. Automated detection and segmentation of dental caries using a novel cascaded learning approach. Biomed Signal Process Control. 2025;102:107344.","journal-title":"Biomed Signal Process Control"},{"key":"1401_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.displa.2021.102119","volume":"71","author":"T Xu","year":"2022","unstructured":"Xu T, et al. Artificial intelligence assisted identification of therapy history from periapical films for dental root canal. Displays. 2022;71:102119.","journal-title":"Displays"},{"key":"1401_CR31","doi-asserted-by":"publisher","DOI":"10.3390\/bioengineering9120777","author":"Y Chuo","year":"2022","unstructured":"Chuo Y, et al. A high-accuracy detection system: based on transfer learning for apical lesions on periapical radiograph. Bioengineering. 2022. https:\/\/doi.org\/10.3390\/bioengineering9120777.","journal-title":"Bioengineering"},{"issue":"1","key":"1401_CR32","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1111\/ipd.13200","volume":"35","author":"T Ma","year":"2025","unstructured":"Ma T, et al. Deep learning-based detection of irreversible pulpitis in primary molars. Int J Paediatr Dent. 2025;35(1):57\u201367. https:\/\/doi.org\/10.1111\/ipd.13200.","journal-title":"Int J Paediatr Dent"},{"issue":"5","key":"1401_CR33","doi-asserted-by":"publisher","first-page":"610","DOI":"10.1016\/j.oooo.2021.01.018","volume":"131","author":"R Pauwels","year":"2021","unstructured":"Pauwels R, et al. Artificial intelligence for detection of periapical lesions on intraoral radiographs: Comparison between convolutional neural networks and human observers. Oral Surg Oral Med Oral Pathol Oral Radiol. 2021;131(5):610\u20136. https:\/\/doi.org\/10.1016\/j.oooo.2021.01.018.","journal-title":"Oral Surg Oral Med Oral Pathol Oral Radiol"},{"issue":"1","key":"1401_CR34","doi-asserted-by":"publisher","first-page":"651","DOI":"10.1007\/s00784-021-04043-y","volume":"26","author":"NP Moidu","year":"2022","unstructured":"Moidu NP, Sharma S, Chawla A, Kumar V, Logani A. Deep learning for categorization of endodontic lesion based on radiographic periapical index scoring system. Clin Oral Investig. 2022;26(1):651\u20138. https:\/\/doi.org\/10.1007\/s00784-021-04043-y.","journal-title":"Clin Oral Investig"},{"issue":"1","key":"1401_CR35","doi-asserted-by":"publisher","first-page":"801","DOI":"10.1186\/s12903-025-06104-0","volume":"25","author":"J Liu","year":"2025","unstructured":"Liu J, et al. A comparative analysis of deep learning models for assisting in the diagnosis of periapical lesions in periapical radiographs. BMC Oral Health. 2025;25(1):801. https:\/\/doi.org\/10.1186\/s12903-025-06104-0.","journal-title":"BMC Oral Health"},{"key":"1401_CR36","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics15060653","author":"\u0130 \u00c7etinkaya","year":"2025","unstructured":"\u00c7etinkaya \u0130, \u00c7atmabacak ED, \u00d6zt\u00fcrk E. Detection of fractured endodontic instruments in periapical radiographs: a comparative study of YOLOv8 and mask R-CNN. Diagnostics Basel. 2025. https:\/\/doi.org\/10.3390\/diagnostics15060653.","journal-title":"Diagnostics Basel"},{"issue":"1","key":"1401_CR37","doi-asserted-by":"publisher","DOI":"10.1186\/s12903-025-05652-9","volume":"25","author":"ED \u00c7atmabacak","year":"2025","unstructured":"\u00c7atmabacak ED, \u00c7etinkaya \u0130. Deep learning algorithms for detecting fractured instruments in root canals. BMC Oral Health. 2025;25(1):293. https:\/\/doi.org\/10.1186\/s12903-025-05652-9.","journal-title":"BMC Oral Health"},{"issue":"3","key":"1401_CR38","doi-asserted-by":"publisher","first-page":"348","DOI":"10.1111\/edt.13027","volume":"41","author":"S Ozsari","year":"2025","unstructured":"Ozsari S, et al. Automatic Vertical Root Fracture Detection on Intraoral Periapical Radiographs With Artificial Intelligence-Based Image Enhancement. Dent Traumatol. 2025;41(3):348\u201362. https:\/\/doi.org\/10.1111\/edt.13027.","journal-title":"Dent Traumatol"},{"issue":"1","key":"1401_CR39","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1186\/s40001-025-02331-x","volume":"30","author":"L Jin","year":"2025","unstructured":"Jin L, et al. Deep-learning network for automated evaluation of root-canal filling radiographic quality. Eur J Med Res. 2025;30(1):297. https:\/\/doi.org\/10.1186\/s40001-025-02331-x.","journal-title":"Eur J Med Res"},{"issue":"12","key":"1401_CR40","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0310925","volume":"19","author":"D Dennis","year":"2024","unstructured":"Dennis D, Suebnukarn S, Vicharueang S, Limprasert W. Development and evaluation of a deep learning segmentation model for assessing non-surgical endodontic treatment outcomes on periapical radiographs: a retrospective study. PLoS One. 2024;19(12):e0310925. https:\/\/doi.org\/10.1371\/journal.pone.0310925.","journal-title":"PLoS One"},{"issue":"1","key":"1401_CR41","doi-asserted-by":"publisher","DOI":"10.1186\/s12903-024-04235-4","volume":"24","author":"H Karkehabadi","year":"2024","unstructured":"Karkehabadi H, Khoshbin E, Ghasemi N, Mahavi A, Mohammad-Rahimi H, Sadr S. Deep learning for determining the difficulty of endodontic treatment: a pilot study. BMC Oral Health. 2024;24(1):574. https:\/\/doi.org\/10.1186\/s12903-024-04235-4.","journal-title":"BMC Oral Health"},{"key":"1401_CR42","doi-asserted-by":"publisher","DOI":"10.3390\/bioengineering10060640","author":"YC Chen","year":"2023","unstructured":"Chen YC. Improving dental implant outcomes: CNN-based system accurately measures degree of peri-implantitis damage on periapical film. Bioengineering (Basel). 2023. https:\/\/doi.org\/10.3390\/bioengineering10060640.","journal-title":"Bioengineering (Basel)"},{"issue":"5","key":"1401_CR43","doi-asserted-by":"publisher","first-page":"918","DOI":"10.11607\/jomi.8684","volume":"36","author":"RP da Mata Santos","year":"2021","unstructured":"da Mata Santos RP, et al. Automated identification of dental implants using artificial intelligence. Int J Oral Maxillofac Implants. 2021;36(5):918\u201323. https:\/\/doi.org\/10.11607\/jomi.8684.","journal-title":"Int J Oral Maxillofac Implants"},{"key":"1401_CR44","doi-asserted-by":"crossref","unstructured":"Kabir T et al. An end-to-end entangled segmentation and classification convolutional neural network for periodontitis stage grading from periapical radiographic images, in 2021 IEEE international conference on bioinformatics and biomedicine (BIBM), 2021: IEEE, pp. 1370\u20131375.","DOI":"10.1109\/BIBM52615.2021.9669422"},{"issue":"5","key":"1401_CR45","doi-asserted-by":"publisher","first-page":"2259","DOI":"10.1007\/s10278-023-00880-3","volume":"36","author":"M Vera","year":"2023","unstructured":"Vera M, et al. Artificial intelligence techniques for automatic detection of peri-implant marginal bone remodeling in intraoral radiographs. J Digit Imaging. 2023;36(5):2259\u201377. https:\/\/doi.org\/10.1007\/s10278-023-00880-3.","journal-title":"J Digit Imaging"},{"issue":"1","key":"1401_CR46","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-42385-7","volume":"13","author":"JH Park","year":"2023","unstructured":"Park JH, Moon HS, Jung HI, Hwang J, Choi YH, Kim JE. Deep learning and clustering approaches for dental implant size classification based on periapical radiographs. Sci Rep. 2023;13(1):16856. https:\/\/doi.org\/10.1038\/s41598-023-42385-7.","journal-title":"Sci Rep"},{"issue":"1","key":"1401_CR47","doi-asserted-by":"publisher","first-page":"550","DOI":"10.1016\/j.jds.2023.09.032","volume":"19","author":"IH Chen","year":"2024","unstructured":"Chen IH, et al. Convolutional-neural-network-based radiographs evaluation assisting in early diagnosis of the periodontal bone loss via periapical radiograph. J Dent Sci. 2024;19(1):550\u20139. https:\/\/doi.org\/10.1016\/j.jds.2023.09.032.","journal-title":"J Dent Sci"},{"issue":"3","key":"1401_CR48","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1007\/s11282-024-00739-5","volume":"40","author":"F Dai","year":"2024","unstructured":"Dai F, et al. Convolutional neural networks combined with classification algorithms for the diagnosis of periodontitis. Oral Radiol. 2024;40(3):357\u201366. https:\/\/doi.org\/10.1007\/s11282-024-00739-5.","journal-title":"Oral Radiol"},{"issue":"5","key":"1401_CR49","doi-asserted-by":"publisher","DOI":"10.7759\/cureus.60550","volume":"16","author":"MB Yavuz","year":"2024","unstructured":"Yavuz MB, et al. Classification of periapical and bitewing radiographs as periodontally healthy or diseased by deep learning algorithms. Cureus. 2024;16(5):e60550. https:\/\/doi.org\/10.7759\/cureus.60550.","journal-title":"Cureus"},{"key":"1401_CR50","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics13233562","author":"H Dujic","year":"2023","unstructured":"Dujic H. Automatized detection of periodontal bone loss on periapical radiographs by vision transformer networks. Diagnostics (Basel). 2023. https:\/\/doi.org\/10.3390\/diagnostics13233562.","journal-title":"Diagnostics (Basel)"},{"issue":"3","key":"1401_CR51","doi-asserted-by":"publisher","first-page":"257","DOI":"10.5624\/isd.20240020","volume":"54","author":"K Vilkomir","year":"2024","unstructured":"Vilkomir K, Phen C, Baldwin F, Cole J, Herndon N, Zhang W. Classification of mandibular molar furcation involvement in periapical radiographs by deep learning. Imaging Sci Dent. 2024;54(3):257\u201363. https:\/\/doi.org\/10.5624\/isd.20240020.","journal-title":"Imaging Sci Dent"},{"key":"1401_CR52","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics14151687","author":"TJ Lin","year":"2024","unstructured":"Lin TJ, et al. Evaluation of the alveolar crest and cemento-enamel junction in periodontitis using object detection on periapical radiographs. Diagnostics (Basel). 2024. https:\/\/doi.org\/10.3390\/diagnostics14151687.","journal-title":"Diagnostics (Basel)"},{"issue":"4 (Supple-4)","key":"1401_CR53","doi-asserted-by":"publisher","first-page":"S5","DOI":"10.47391\/jpma.aku-9s-02","volume":"74","author":"N Adnan","year":"2024","unstructured":"Adnan N, Hanif M, Khan K, Faridoon F, Umer F. An artificial intelligence model for implant segmentation on periapical radiographs. J Pak Med Assoc. 2024;74(4 (Supple-4)):S5-s9. https:\/\/doi.org\/10.47391\/jpma.aku-9s-02.","journal-title":"J Pak Med Assoc"},{"issue":"5","key":"1401_CR54","doi-asserted-by":"publisher","first-page":"578","DOI":"10.1111\/clr.14406","volume":"36","author":"P Fasih","year":"2025","unstructured":"Fasih P, Yari A, Kamali Hakim L, Kashe NN. Artificial intelligence in detecting and segmenting vertical misfit of prosthesis in radiographic images of dental implants: a cross-sectional analysis. Clin Oral Implants Res. 2025;36(5):578\u201388. https:\/\/doi.org\/10.1111\/clr.14406.","journal-title":"Clin Oral Implants Res"},{"key":"1401_CR55","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics15101194","author":"YJ Lin","year":"2025","unstructured":"Lin YJ, et al. Deep learning-assisted diagnostic system: implant brand detection using improved IB-YOLOv10 in periapical radiographs. Diagnostics (Basel). 2025. https:\/\/doi.org\/10.3390\/diagnostics15101194.","journal-title":"Diagnostics (Basel)"},{"issue":"1","key":"1401_CR56","doi-asserted-by":"publisher","first-page":"545","DOI":"10.1007\/s10278-025-01405-w","volume":"38","author":"B \u015eahin","year":"2025","unstructured":"\u015eahin B, Eninan\u00e7 \u0130. A deep learning-based approach to detect lamina dura loss on periapical radiographs. J Imaging Inform Med. 2025;38(1):545\u201355. https:\/\/doi.org\/10.1007\/s10278-025-01405-w.","journal-title":"J Imaging Inform Med"},{"issue":"3","key":"1401_CR57","doi-asserted-by":"publisher","DOI":"10.1259\/dmfr.20210246","volume":"51","author":"C G\u00f6r\u00fcrg\u00f6z","year":"2022","unstructured":"G\u00f6r\u00fcrg\u00f6z C, et al. Performance of a convolutional neural network algorithm for tooth detection and numbering on periapical radiographs. Dentomaxillofac Radiol. 2022;51(3):20210246. https:\/\/doi.org\/10.1259\/dmfr.20210246.","journal-title":"Dentomaxillofac Radiol"},{"issue":"3","key":"1401_CR58","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1007\/s11548-021-02321-4","volume":"16","author":"N Kyventidis","year":"2021","unstructured":"Kyventidis N, Angelopoulos C. Intraoral radiograph anatomical region classification using neural networks. Int J Comput Assist Radiol Surg. 2021;16(3):447\u201355. https:\/\/doi.org\/10.1007\/s11548-021-02321-4.","journal-title":"Int J Comput Assist Radiol Surg"},{"issue":"1","key":"1401_CR59","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1007\/s11282-021-00538-2","volume":"38","author":"M Mori","year":"2022","unstructured":"Mori M, et al. Performance of deep learning technology for evaluation of positioning quality in periapical radiography of the maxillary canine. Oral Radiol. 2022;38(1):147\u201354. https:\/\/doi.org\/10.1007\/s11282-021-00538-2.","journal-title":"Oral Radiol"},{"key":"1401_CR60","doi-asserted-by":"publisher","DOI":"10.1016\/j.jdent.2023.104588","volume":"135","author":"L Pf\u00e4nder","year":"2023","unstructured":"Pf\u00e4nder L, Schneider L, B\u00fcttner M, Krois J, Meyer-Lueckel H, Schwendicke F. Multi-modal deep learning for automated assembly of periapical radiographs. J Dent. 2023;135:104588. https:\/\/doi.org\/10.1016\/j.jdent.2023.104588.","journal-title":"J Dent"},{"issue":"11","key":"1401_CR61","doi-asserted-by":"publisher","DOI":"10.1007\/s00784-024-05999-3","volume":"28","author":"H Ayy\u0131ld\u0131z","year":"2024","unstructured":"Ayy\u0131ld\u0131z H, Orhan M, Bilgir E, \u00c7elik \u00d6, Bayrakdar \u0130. Tooth numbering with polygonal segmentation on periapical radiographs: an artificial intelligence study. Clin Oral Investig. 2024;28(11):610. https:\/\/doi.org\/10.1007\/s00784-024-05999-3.","journal-title":"Clin Oral Investig"},{"key":"1401_CR62","doi-asserted-by":"publisher","DOI":"10.3390\/bioengineering10080911","author":"IDS Chen","year":"2023","unstructured":"Chen IDS, Yang CM, Chen MJ, Chen MC, Weng RM, Yeh CH. Deep learning-based recognition of periodontitis and dental caries in dental x-ray images. Bioengineering (Basel). 2023. https:\/\/doi.org\/10.3390\/bioengineering10080911.","journal-title":"Bioengineering (Basel)"},{"issue":"6","key":"1401_CR63","doi-asserted-by":"publisher","first-page":"711","DOI":"10.1016\/j.oooo.2020.08.024","volume":"131","author":"HA Khan","year":"2021","unstructured":"Khan HA, et al. Automated feature detection in dental periapical radiographs by using deep learning. Oral Surg Oral Med Oral Pathol Oral Radiol. 2021;131(6):711\u201320. https:\/\/doi.org\/10.1016\/j.oooo.2020.08.024.","journal-title":"Oral Surg Oral Med Oral Pathol Oral Radiol"},{"issue":"3","key":"1401_CR64","doi-asserted-by":"publisher","first-page":"1301","DOI":"10.1016\/j.jds.2023.03.020","volume":"18","author":"CC Chen","year":"2023","unstructured":"Chen CC, et al. Automatic recognition of teeth and periodontal bone loss measurement in digital radiographs using deep-learning artificial intelligence. J Dent Sci. 2023;18(3):1301\u20139. https:\/\/doi.org\/10.1016\/j.jds.2023.03.020.","journal-title":"J Dent Sci"},{"issue":"7","key":"1401_CR65","doi-asserted-by":"publisher","DOI":"10.1259\/dmfr.20230284","volume":"52","author":"N Turosz","year":"2023","unstructured":"Turosz N, Ch\u0119ci\u0144ska K, Ch\u0119ci\u0144ski M, Brzozowska A, Nowak Z, Sikora M. Applications of artificial intelligence in the analysis of dental panoramic radiographs: an overview of systematic reviews. Dentomaxillofac Radiol. 2023;52(7):20230284. https:\/\/doi.org\/10.1259\/dmfr.20230284.","journal-title":"Dentomaxillofac Radiol"},{"issue":"1","key":"1401_CR66","doi-asserted-by":"publisher","first-page":"508","DOI":"10.1016\/j.jds.2020.06.019","volume":"16","author":"SB Khanagar","year":"2021","unstructured":"Khanagar SB, et al. Developments, application, and performance of artificial intelligence in dentistry \u2013 a systematic review. J Dent Sci. 2021;16(1):508\u201322. https:\/\/doi.org\/10.1016\/j.jds.2020.06.019.","journal-title":"J Dent Sci"},{"issue":"11","key":"1401_CR67","doi-asserted-by":"publisher","DOI":"10.7759\/cureus.98061","volume":"17","author":"SH Khan","year":"2025","unstructured":"Khan SH, Alazmi SO, Ansari ST, Khan S. Comparative evaluation of digital panoramic and digital intraoral periapical radiographic techniques for the assessment of interproximal bone loss: a cross-sectional study. Cureus. 2025;17(11):e98061. https:\/\/doi.org\/10.7759\/cureus.98061.","journal-title":"Cureus"},{"key":"1401_CR68","doi-asserted-by":"publisher","DOI":"10.3389\/fdmed.2025.1685359","author":"P Prasad","year":"2026","unstructured":"Prasad P. The role of artificial intelligence in diagnosing pediatric dental disorders\u2014a narrative review. Frontiers in Dental Medicine. 2026. https:\/\/doi.org\/10.3389\/fdmed.2025.1685359.","journal-title":"Frontiers in Dental Medicine"},{"key":"1401_CR69","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclinepi.2024.111370","volume":"171","author":"E Tomlinson","year":"2024","unstructured":"Tomlinson E, et al. Common challenges and suggestions for risk of bias tool development: a systematic review of methodological studies. J Clin Epidemiol. 2024;171:111370. https:\/\/doi.org\/10.1016\/j.jclinepi.2024.111370.","journal-title":"J Clin Epidemiol"},{"issue":"1","key":"1401_CR70","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1038\/s41746-021-00544-y","volume":"5","author":"S Jayakumar","year":"2022","unstructured":"Jayakumar S, et al. Quality assessment standards in artificial intelligence diagnostic accuracy systematic reviews: a meta-research study. NPJ Digit Med. 2022;5(1):11. https:\/\/doi.org\/10.1038\/s41746-021-00544-y.","journal-title":"NPJ Digit Med"},{"issue":"10","key":"1401_CR71","doi-asserted-by":"publisher","first-page":"1659","DOI":"10.1038\/s41591-021-01517-0","volume":"27","author":"V Sounderajah","year":"2021","unstructured":"Sounderajah V, et al. A quality assessment tool for artificial intelligence-centered diagnostic test accuracy studies: QUADAS-AI. Nat Med. 2021;27(10):1659\u201365. https:\/\/doi.org\/10.1038\/s41591-021-01517-0.","journal-title":"Nat Med"},{"key":"1401_CR72","first-page":"289","volume-title":"The Reference Manual of Pediatric Dentistry","author":"American Academy of Pediatric Dentistry (AAPD)","year":"2023","unstructured":"American Academy of Pediatric Dentistry (AAPD). Prescribing Dental Radiographs for Infants, Children, Adolescents, and Individuals with Special Health Care Needs. The Reference Manual of Pediatric Dentistry. Chicago, Ill.: AAPD; 2023. pp. 289\u201391."},{"issue":"6","key":"1401_CR73","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1111\/j.1365-2591.2008.01530.x","volume":"42","author":"S Patel","year":"2009","unstructured":"Patel S. New dimensions in endodontic imaging: part 1. Conventional and alternative radiographic systems. Int Endod J. 2009;42(6):447\u201362. https:\/\/doi.org\/10.1111\/j.1365-2591.2008.01530.x.","journal-title":"Int Endod J"}],"container-title":["Odontology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10266-026-01401-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10266-026-01401-8","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10266-026-01401-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T21:15:13Z","timestamp":1790889313000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10266-026-01401-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,13]]},"references-count":73,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2026,10]]}},"alternative-id":["1401"],"URL":"https:\/\/doi.org\/10.1007\/s10266-026-01401-8","relation":{},"ISSN":["1618-1247","1618-1255"],"issn-type":[{"value":"1618-1247","type":"print"},{"value":"1618-1255","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,13]]},"assertion":[{"value":"5 March 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 April 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 May 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","label":"Conflict of interest","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable, as this study is a bibliometric analysis of already published literature.","order":2,"name":"Ethics","label":"Ethical approval","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":3,"name":"Ethics","label":"Informed consent","group":{"name":"EthicsHeading","label":"Declarations"}}]}}