{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T13:55:24Z","timestamp":1789048524012,"version":"build-2803163510"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,7,9]],"date-time":"2025-07-09T00:00:00Z","timestamp":1752019200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,7,9]],"date-time":"2025-07-09T00:00:00Z","timestamp":1752019200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Oral Radiol"],"published-print":{"date-parts":[[2026,1]]},"DOI":"10.1007\/s11282-025-00839-w","type":"journal-article","created":{"date-parts":[[2025,7,10]],"date-time":"2025-07-10T09:38:01Z","timestamp":1752140281000},"page":"1-12","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Applicability and performance of convolutional neural networks for the identification of periodontal bone loss in periapical radiographs: a scoping review"],"prefix":"10.1007","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0622-3892","authenticated-orcid":false,"given":"Ramadhan Hardani","family":"Putra","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eha Renwi","family":"Astuti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aga Satria","family":"Nurrachman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunita","family":"Savitri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anastasya Vara","family":"Vadya","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Serafina Tasyarani","family":"Khairunisa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Masahiro","family":"Iikubo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,7,9]]},"reference":[{"key":"839_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1128\/mbio.01926-14","volume":"6","author":"B Shi","year":"2015","unstructured":"Shi B, Chang M, Martin J, Mitreva M, Lux R, Klokkevold P, et al. Dynamic changes in the subgingival microbiome and their potential for diagnosis and prognosis of periodontitis. MBio. 2015;6:1\u201311. https:\/\/doi.org\/10.1128\/mbio.01926-14.","journal-title":"MBio"},{"issue":"1","key":"839_CR2","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1111\/prd.12331","volume":"84","author":"G Hajishengallis","year":"2020","unstructured":"Hajishengallis G, Chavakis T, Lambris JD. Current understanding of periodontal disease pathogenesis and targets for host-modulation therapy. Periodontology. 2020;84(1):14\u201334. https:\/\/doi.org\/10.1111\/prd.12331.","journal-title":"Periodontology 2000"},{"key":"839_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12967-020-02664-7","volume":"18","author":"X Huang","year":"2020","unstructured":"Huang X, Xie M, Xie Y, Mei F, Lu X, Li X, et al. The roles of osteocytes in alveolar bone destruction in periodontitis. J Transl Med. 2020;18:1\u201315. https:\/\/doi.org\/10.1186\/s12967-020-02664-7.","journal-title":"J Transl Med"},{"key":"839_CR4","doi-asserted-by":"publisher","first-page":"S149","DOI":"10.1002\/jper.18-0006","volume":"45","author":"MS Tonetti","year":"2018","unstructured":"Tonetti MS, Greenwell H, Kornman KS. Staging and grading of periodontitis: framework and proposal of a new classification and case definition. J Clin Periodontol. 2018;45:S149\u201361. https:\/\/doi.org\/10.1002\/jper.18-0006.","journal-title":"J Clin Periodontol"},{"key":"839_CR5","doi-asserted-by":"publisher","first-page":"S162","DOI":"10.1111\/jcpe.12946","volume":"45","author":"PN Papapanou","year":"2018","unstructured":"Papapanou PN, Sanz M, Buduneli N, Dietrich T, Feres M, Fine DH, et al. Periodontitis: consensus report of workgroup 2 of the 2017 World Workshop on the classification of periodontal and peri-implant diseases and conditions. J Clin Periodontol. 2018;45:S162\u201370. https:\/\/doi.org\/10.1111\/jcpe.12946.","journal-title":"J Clin Periodontol"},{"issue":"1","key":"839_CR6","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1111\/prd.12580","volume":"95","author":"R Jacobs","year":"2024","unstructured":"Jacobs R, Fontenele RC, Lahoud P, Shujaat S, Bornstein MM. Radiographic diagnosis of periodontal diseases\u2013current evidence versus innovations. Periodontology. 2024;95(1):51\u201369. https:\/\/doi.org\/10.1111\/prd.12580.","journal-title":"Periodontology 2000"},{"issue":"1","key":"839_CR7","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1111\/prd.12193","volume":"74","author":"WC Scarfe","year":"2017","unstructured":"Scarfe WC, Azevedo B, Pinheiro LR, Priaminiarti M, Sales MA. The emerging role of maxillofacial radiology in the diagnosis and management of patients with complex periodontitis. Periodontology. 2017;74(1):116\u201339. https:\/\/doi.org\/10.1111\/prd.12193.","journal-title":"Periodontol 2000"},{"key":"839_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-019-44839-3","volume":"9","author":"J Krois","year":"2019","unstructured":"Krois J, Ekert T, Meinhold L, Golla T, Kharbot B, Wittemeier A, et al. Deep learning for the radiographic detection of periodontal bone loss. Sci Rep. 2019;9:1\u20136. https:\/\/doi.org\/10.1038\/s41598-019-44839-3.","journal-title":"Sci Rep"},{"key":"839_CR9","doi-asserted-by":"publisher","first-page":"854","DOI":"10.1111\/jcpe.13679","volume":"49","author":"P Winkler","year":"2022","unstructured":"Winkler P, Dannewitz B, Nickles K, Petsos H, Eickholz P. Assessment of periodontitis grade in epidemiological studies using interdental attachment loss instead of radiographic bone loss. J Clin Periodontol. 2022;49:854\u201361. https:\/\/doi.org\/10.1111\/jcpe.13679.","journal-title":"J Clin Periodontol"},{"issue":"1108","key":"839_CR10","doi-asserted-by":"publisher","first-page":"20190840","DOI":"10.1259\/bjr.20190840","volume":"93","author":"M Hardy","year":"2020","unstructured":"Hardy M, Harvey H. Artificial intelligence in diagnostic imaging: Impact on the radiography profession. Br J Radiol. 2020;93(1108):20190840. https:\/\/doi.org\/10.1259\/bjr.20190840.","journal-title":"Br J Radiol"},{"key":"839_CR11","doi-asserted-by":"publisher","first-page":"590","DOI":"10.1148\/radiol.2018180547","volume":"290","author":"S Soffer","year":"2019","unstructured":"Soffer S, Ben-Cohen A, Shimon O, Amitai MM, Greenspan H, Klang E. Convolutional neural networks for radiologic images: a radiologist\u2019s guide. Radiology. 2019;290:590\u2013606. https:\/\/doi.org\/10.1148\/radiol.2018180547.","journal-title":"Radiology"},{"issue":"1","key":"839_CR12","doi-asserted-by":"publisher","first-page":"20210197","DOI":"10.1259\/dmfr.20210197","volume":"51","author":"RH Putra","year":"2022","unstructured":"Putra RH, Doi C, Yoda N, Astuti ER, Sasaki K. Current applications and development of artificial intelligence for digital dental radiography. Dentomaxillofacial Radiol. 2022;51(1):20210197. https:\/\/doi.org\/10.1259\/dmfr.20210197.","journal-title":"Dentomaxillofacial Radiol"},{"key":"839_CR13","doi-asserted-by":"publisher","first-page":"383","DOI":"10.5624\/isd.20220105","volume":"52","author":"R Widyaningrum","year":"2022","unstructured":"Widyaningrum R, Candradewi I, Aji NRAS, Aulianisa R. Comparison of multi-label U-Net and mask R-CNN for panoramic radiograph segmentation to detect periodontitis. Imag Sci Dent. 2022;52:383\u201391. https:\/\/doi.org\/10.5624\/isd.20220105.","journal-title":"Imag Sci Dent"},{"key":"839_CR14","doi-asserted-by":"publisher","first-page":"795","DOI":"10.1016\/j.adaj.2023.05.010","volume":"154","author":"S Patil","year":"2023","unstructured":"Patil S, Joda T, Soffe B, Awan KH, Fageeh HN, Tovani-Palone MR, et al. Efficacy of artificial intelligence in the detection of periodontal bone loss and classification of periodontal diseases: a systematic review. J Am Dent Assoc. 2023;154:795\u2013804. https:\/\/doi.org\/10.1016\/j.adaj.2023.05.010.","journal-title":"J Am Dent Assoc"},{"key":"839_CR15","doi-asserted-by":"publisher","first-page":"1682","DOI":"10.1007\/s11547-024-01882-z","volume":"129","author":"G Stera","year":"2024","unstructured":"Stera G, Giusti M, Magnini A, Calistri L, Izzetti R, Nardi C. Diagnostic accuracy of periapical radiography and panoramic radiography in the detection of apical periodontitis: a systematic review and meta-analysis. Radiol Med. 2024;129:1682\u201395. https:\/\/doi.org\/10.1007\/s11547-024-01882-z.","journal-title":"Radiol Med"},{"key":"839_CR16","doi-asserted-by":"publisher","first-page":"467","DOI":"10.7326\/m18-0850","volume":"169","author":"AC Tricco","year":"2018","unstructured":"Tricco AC, Lillie E, Zarin W, O\u2019Brien KK, Colquhoun H, Levac D, et al. PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation. Ann Intern Med. 2018;169:467\u201373. https:\/\/doi.org\/10.7326\/m18-0850.","journal-title":"Ann Intern Med"},{"key":"839_CR17","doi-asserted-by":"publisher","first-page":"114","DOI":"10.5051\/jpis.2018.48.2.114","volume":"48","author":"J Lee","year":"2018","unstructured":"Lee J, Kim D, Jeong S-N, Choi S-H. Diagnosis and prediction of periodontally compromised teeth using a deep learning-based convolutional neural network algorithm. J Periodontal Implant Sci. 2018;48:114\u201323. https:\/\/doi.org\/10.5051\/jpis.2018.48.2.114.","journal-title":"J Periodontal Implant Sci"},{"key":"839_CR18","doi-asserted-by":"publisher","unstructured":"Moran MBH, Faria M, Giraldi G, Bastos L, Da Silva IB, Conci A. On using convolutional neural networks to classify periodontal bone destruction in periapical radiographs. Proc \u2013 2020 IEEE Int Conf Bioinforma Biomed BIBM. 2020;2020:2036\u20139. https:\/\/doi.org\/10.1109\/BIBM49941.2020.9313501.","DOI":"10.1109\/BIBM49941.2020.9313501"},{"issue":"5","key":"839_CR19","doi-asserted-by":"publisher","first-page":"e60550","DOI":"10.7759\/cureus.60550","volume":"16","author":"MB Yavuz","year":"2024","unstructured":"Yavuz MB, Sali N, Kurt Bayrakdar S, Ek\u015fi C, \u0130mamo\u011flu BS, Bayrakdar \u0130\u015e, et al. Classification of periapical and bitewing radiographs as periodontally healthy or diseased by deep learning algorithms. Cureus. 2024. https:\/\/doi.org\/10.7759\/cureus.60550.","journal-title":"Cureus"},{"key":"839_CR20","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, Haider MA, Ansari HA, Ishaq H, Kiyani A, Sohail K, et al. Automated feature detection in dental periapical radiographs by using deep learning. Oral Surg Oral Med Oral Pathol Oral Radiol. 2021;131:711\u201320. https:\/\/doi.org\/10.1016\/j.oooo.2020.08.024.","journal-title":"Oral Surg Oral Med Oral Pathol Oral Radiol"},{"key":"839_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/s21062013","volume":"21","author":"M Moran","year":"2021","unstructured":"Moran M, Faria M, Giraldi G, Bastos L, Conci A. Do radiographic assessments of periodontal bone loss improve with deep learning methods for enhanced image resolution? Sensors. 2021;21:1\u201324. https:\/\/doi.org\/10.3390\/s21062013.","journal-title":"Sensors"},{"key":"839_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12903-022-02436-3","volume":"22","author":"G Alotaibi","year":"2022","unstructured":"Alotaibi G, Awawdeh M, Farook FF, Aljohani M, Aldhafiri RM, Aldhoayan M. Artificial intelligence (AI) diagnostic tools: utilizing a convolutional neural network (CNN) to assess periodontal bone level radiographically\u2014a retrospective study. BMC Oral Health. 2022;22:1\u20137. https:\/\/doi.org\/10.1186\/s12903-022-02436-3.","journal-title":"BMC Oral Health"},{"key":"839_CR23","doi-asserted-by":"publisher","first-page":"621","DOI":"10.1016\/j.identj.2022.02.009","volume":"72","author":"N Tsoromokos","year":"2022","unstructured":"Tsoromokos N, Parinussa S, Claessen F, Moin DA, Loos BG. Estimation of alveolar bone loss in periodontitis using machine learning. Int Dent J. 2022;72:621\u20137. https:\/\/doi.org\/10.1016\/j.identj.2022.02.009.","journal-title":"Int Dent J"},{"key":"839_CR24","doi-asserted-by":"publisher","first-page":"6629","DOI":"10.1007\/s00784-022-04617-4","volume":"26","author":"J Chang","year":"2022","unstructured":"Chang J, Chang MF, Angelov N, Hsu CY, Meng HW, Sheng S, et al. Application of deep machine learning for the radiographic diagnosis of periodontitis. Clin Oral Investig. 2022;26:6629\u201337. https:\/\/doi.org\/10.1007\/s00784-022-04617-4.","journal-title":"Clin Oral Investig"},{"key":"839_CR25","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, Wu YF, Aung LM, Lin JCY, Ngo ST, Su JN, et al. Automatic recognition of teeth and periodontal bone loss measurement in digital radiographs using deep-learning artificial intelligence. J Dent Sci. 2023;18:1301\u20139. https:\/\/doi.org\/10.1016\/j.jds.2023.03.020.","journal-title":"J Dent Sci"},{"issue":"22","key":"839_CR26","doi-asserted-by":"publisher","first-page":"7189","DOI":"10.3390\/jcm12227189","volume":"12","author":"P Hoss","year":"2023","unstructured":"Hoss P, Meyer O, W\u00f6lfle UC, W\u00fclk A, Meusburger T, Meier L, et al. Detection of periodontal bone loss on periapical radiographs\u2014a diagnostic study using different convolutional neural networks. J Clin Med. 2023;12(22):7189. https:\/\/doi.org\/10.3390\/jcm12227189.","journal-title":"J Clin Med"},{"key":"839_CR27","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, Lin CH, Lee MK, Chen TE, Lan TH, Chang CM, et al. Convolutional-neural-network-based radiographs evaluation assisting in early diagnosis of the periodontal bone loss via periapical radiograph. J Dent Sci. 2024;19:550\u20139. https:\/\/doi.org\/10.1016\/j.jds.2023.09.032.","journal-title":"J Dent Sci"},{"issue":"1","key":"839_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1902\/annals.1999.4.1.1","volume":"4","author":"GC Armitage","year":"1999","unstructured":"Armitage GC. Development of a classification system for periodontal diseases and conditions. Ann Periodontol. 1999;4(1):1\u20136. https:\/\/doi.org\/10.1902\/annals.1999.4.1.1.","journal-title":"Ann Periodontol"},{"key":"839_CR29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-024-56706-x","volume":"14","author":"O Rainio","year":"2024","unstructured":"Rainio O, Teuho J, Kl\u00e9n R. Evaluation metrics and statistical tests for machine learning. Sci Rep. 2024;14:1\u201314. https:\/\/doi.org\/10.1038\/s41598-024-56706-x.","journal-title":"Sci Rep"},{"key":"839_CR30","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1186\/s13104-022-06096-y","volume":"15","author":"DP M\u00fcller","year":"2022","unstructured":"M\u00fcller DP, Pereira S, Alves V. Towards a guideline for evaluation metrics in medical image segmentation. BMC Res Notes. 2022;15:102. https:\/\/doi.org\/10.1186\/s13104-022-06096-y.","journal-title":"BMC Res Notes"},{"key":"839_CR31","doi-asserted-by":"publisher","first-page":"352","DOI":"10.1002\/JPER.19-0671","volume":"91","author":"KS Kornman","year":"2020","unstructured":"Kornman KS, Papapanou PN. Clinical application of the new classification of periodontal diseases: ground rules, clarifications, and \u201cgray zones.\u201d J Periodontol. 2020;91:352\u201360. https:\/\/doi.org\/10.1002\/JPER.19-0671.","journal-title":"J Periodontol"},{"key":"839_CR32","doi-asserted-by":"publisher","unstructured":"Salvi GE, Roccuzzo A, Imber JC, St\u00e4hli A, Klinge B, Lang NP. Clinical periodontal diagnosis. Periodontol. 2000;2023:1\u201319. https:\/\/doi.org\/10.1111\/prd.12487.","DOI":"10.1111\/prd.12487"},{"issue":"1","key":"839_CR33","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1034\/j.1600-0722.2000.00729.x","volume":"108","author":"P Eickholz","year":"2000","unstructured":"Eickholz P, Hausmann E. Accuracy of radiographic assessment of interproximal bone loss in intrabony defects using linear measurements. Eur J Oral Sci. 2000;108(1):70\u20133. https:\/\/doi.org\/10.1034\/j.1600-0722.2000.00729.x.","journal-title":"Eur J Oral Sci"},{"issue":"4","key":"839_CR34","doi-asserted-by":"publisher","first-page":"611","DOI":"10.1007\/s13244-018-0639-9","volume":"9","author":"R Yamashita","year":"2018","unstructured":"Yamashita R, Nishio M, Do RKG, Togashi K. Convolutional neural networks: an overview and application in radiology. Insights Imag. 2018;9(4):611\u201329. https:\/\/doi.org\/10.1007\/s13244-018-0639-9.","journal-title":"Insights Imag"},{"key":"839_CR35","doi-asserted-by":"publisher","DOI":"10.1109\/ICICIP.2018.8606702","author":"S Gu","year":"2018","unstructured":"Gu S, Ding L. A complex-valued VGG network based deep learing algorithm for image recognition. 9th Int Conf Intell Control Inf Process ICICIP. 2018. https:\/\/doi.org\/10.1109\/ICICIP.2018.8606702.","journal-title":"9th Int Conf Intell Control Inf Process ICICIP"},{"issue":"4","key":"839_CR36","doi-asserted-by":"publisher","first-page":"1680","DOI":"10.3390\/make5040083","volume":"5","author":"J Terven","year":"2023","unstructured":"Terven J, C\u00f3rdova-Esparza D-M, Romero-Gonz\u00e1lez J-A. A Comprehensive review of YOLO architectures in computer vision: from YOLOv1 to YOLOv8 and YOLO-NAS. Mach Learn Knowl Extr. 2023;5(4):1680\u2013716. https:\/\/doi.org\/10.3390\/make5040083.","journal-title":"Mach Learn Knowl Extr"},{"key":"839_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106374","volume":"152","author":"Z Kong","year":"2023","unstructured":"Kong Z, Ouyang H, Cao Y, Huang T, Ahn E, Zhang M, et al. Automated periodontitis bone loss diagnosis in panoramic radiographs using a bespoke two-stage detector. Comput Biol Med. 2023;152: 106374. https:\/\/doi.org\/10.1016\/j.compbiomed.2022.106374.","journal-title":"Comput Biol Med"},{"key":"839_CR38","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12903-022-02119-z","volume":"22","author":"L Jiang","year":"2022","unstructured":"Jiang L, Chen D, Cao Z, Wu F, Zhu H, Zhu F. A two-stage deep learning architecture for radiographic staging of periodontal bone loss. BMC Oral Health. 2022;22:1\u20139. https:\/\/doi.org\/10.1186\/s12903-022-02119-z.","journal-title":"BMC Oral Health"},{"key":"839_CR39","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2021.638182","volume":"11","author":"R Yang","year":"2021","unstructured":"Yang R, Yu Y. Artificial convolutional neural network in object detection and semantic segmentation for medical imaging analysis. Front Oncol. 2021;11: 638182. https:\/\/doi.org\/10.3389\/fonc.2021.638182.","journal-title":"Front Oncol"},{"key":"839_CR40","doi-asserted-by":"publisher","DOI":"10.1109\/WCSP52459.2021.9613447","author":"W Yan","year":"2021","unstructured":"Yan W, Chen C, Zhang D. U-Net-based medical image segmentation algorithm. 13th Int Conf Wirel Commun Signal Process WCSP. 2021. https:\/\/doi.org\/10.1109\/WCSP52459.2021.9613447.","journal-title":"13th Int Conf Wirel Commun Signal Process WCSP"},{"key":"839_CR41","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.48550\/arXiv.1511.00561","volume":"39","author":"V Badrinarayanan","year":"2017","unstructured":"Badrinarayanan V, Kendall A, Cipolla R. Segnet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell. 2017;39:2481\u201395. https:\/\/doi.org\/10.48550\/arXiv.1511.00561.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"839_CR42","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1148\/radiol.2020192224","volume":"295","author":"MJ Willemink","year":"2020","unstructured":"Willemink MJ, Koszek WA, Hardell C, Wu J, Fleischmann D, Harvey H, et al. Preparing medical imaging data for machine learning. Radiology. 2020;295:4\u201315. https:\/\/doi.org\/10.1148\/radiol.2020192224.","journal-title":"Radiology"},{"key":"839_CR43","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpbup.2024.100146","volume":"5","author":"M Khalifa","year":"2024","unstructured":"Khalifa M, Albadawy M. AI in diagnostic imaging: Revolutionising accuracy and efficiency. Comput Methods Prog Biomed Updat. 2024;5: 100146. https:\/\/doi.org\/10.1016\/j.cmpbup.2024.100146.","journal-title":"Comput Methods Prog Biomed Updat"},{"key":"839_CR44","doi-asserted-by":"publisher","DOI":"10.1148\/ryai.2020200029","volume":"2","author":"J Mongan","year":"2020","unstructured":"Mongan J, Moy L, Kahn CE. Checklist for artificial intelligence in medical imaging (CLAIM): a guide for authors and reviewers. Radiol Artif Intell. 2020;2: e200029. https:\/\/doi.org\/10.1148\/ryai.2020200029.","journal-title":"Radiol Artif Intell"},{"key":"839_CR45","doi-asserted-by":"publisher","first-page":"64606","DOI":"10.1109\/ACCESS.2021.3074243","volume":"9","author":"L Wang","year":"2021","unstructured":"Wang L, Han M, Li X, Zhang N, Cheng H. Review of classification methods on unbalanced data sets. IEEE Access. 2021;9:64606\u201328. https:\/\/doi.org\/10.1109\/ACCESS.2021.3074243.","journal-title":"IEEE Access"},{"key":"839_CR46","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1148\/radiol.2272012051","volume":"227","author":"J Eng","year":"2003","unstructured":"Eng J. Sample size estimation: how many individuals should be studied? Radiology. 2003;227:309\u201313. https:\/\/doi.org\/10.1148\/radiol.2272012051.","journal-title":"Radiology"},{"key":"839_CR47","doi-asserted-by":"publisher","first-page":"1767","DOI":"10.1007\/s10278-024-01018-9","volume":"37","author":"R Escobar D\u00edaz Guerrero","year":"2024","unstructured":"Escobar D\u00edaz Guerrero R, Carvalho L, Bocklitz T, Popp J, Oliveira JL. A data augmentation methodology to reduce the class imbalance in histopathology images. J Imag Inform Med. 2024;37:1767\u201382. https:\/\/doi.org\/10.1007\/s10278-024-01018-9.","journal-title":"J Imag Inform Med"},{"key":"839_CR48","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1016\/j.sdentj.2019.04.006","volume":"31","author":"N Babay","year":"2019","unstructured":"Babay N, Alshehri F, Al RR. Majors highlights of the new 2017 classification of periodontal and peri-implant diseases and conditions. Saudi Dent J. 2019;31:303\u20135. https:\/\/doi.org\/10.1016\/j.sdentj.2019.04.006.","journal-title":"Saudi Dent J"}],"container-title":["Oral Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11282-025-00839-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11282-025-00839-w","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11282-025-00839-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T04:50:46Z","timestamp":1767847846000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11282-025-00839-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,9]]},"references-count":48,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1]]}},"alternative-id":["839"],"URL":"https:\/\/doi.org\/10.1007\/s11282-025-00839-w","relation":{},"ISSN":["0911-6028","1613-9674"],"issn-type":[{"value":"0911-6028","type":"print"},{"value":"1613-9674","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,9]]},"assertion":[{"value":"13 October 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 June 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 July 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":"The authors have no competing interests to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article does not contain any studies with human or animal subjects performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"This article does not contain any studies with human or animal subjects performed by any of the authors.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}]}}