{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,9]],"date-time":"2026-11-09T00:12:30Z","timestamp":1794183150292,"version":"4.8.0"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,2,10]],"date-time":"2025-02-10T00:00:00Z","timestamp":1739145600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,2,10]],"date-time":"2025-02-10T00:00:00Z","timestamp":1739145600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Oral Health"],"DOI":"10.1186\/s12903-025-05577-3","type":"journal-article","created":{"date-parts":[[2025,2,10]],"date-time":"2025-02-10T11:21:13Z","timestamp":1739186473000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["Detection of dental caries under fixed dental prostheses by analyzing digital panoramic radiographs with artificial intelligence algorithms based on deep learning methods"],"prefix":"10.1186","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3388-8037","authenticated-orcid":false,"given":"Bet\u00fcl","family":"Ayhan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5463-8064","authenticated-orcid":false,"given":"Enes","family":"Ayan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7011-3375","authenticated-orcid":false,"given":"Saadet","family":"Ats\u00fc","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,2,10]]},"reference":[{"issue":"3","key":"5577_CR1","doi-asserted-by":"publisher","first-page":"167","DOI":"10.4317\/jced.50690","volume":"4","author":"A Sharma","year":"2012","unstructured":"Sharma A, Rahul GR, Poduval ST, Shetty K. Removal of failed crown and bridge. J Clin Exp Dent. 2012;4(3):167\u201372.","journal-title":"J Clin Exp Dent"},{"issue":"1","key":"5577_CR2","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1002\/cre2.696","volume":"9","author":"A Alenezi","year":"2023","unstructured":"Alenezi A, Alkhudhayri O, Altowaijri F, Aloufi L, Alharbi F, Alrasheed M, et al. Secondary caries in fixed dental prostheses: long-term clinical evaluation. J Clin Exp Dent. 2023;9(1):249\u201357.","journal-title":"J Clin Exp Dent"},{"issue":"3","key":"5577_CR3","doi-asserted-by":"publisher","first-page":"314","DOI":"10.1067\/mpr.2002.128122","volume":"88","author":"A Zoellner","year":"2002","unstructured":"Zoellner A, Heuermann M, Weber HP, Gaengler P. Secondary caries in crowned teeth: correlation of clinical and radiographic findings. J Prosthet Dent. 2002;88(3):314\u20139.","journal-title":"J Prosthet Dent"},{"issue":"8","key":"5577_CR4","first-page":"10","volume":"10","author":"PR Vedpathak","year":"2016","unstructured":"Vedpathak PR, Gondivkar SM, Bhoosreddy AR, Shah KR, Verma GR, Mehrotra GP, et al. Cone Beam Computed Tomography- An Effective Tool in detecting Caries under fixed Dental Prostheses. J Clin Diagn Res. 2016;10(8):10\u20133.","journal-title":"J Clin Diagn Res"},{"issue":"2","key":"5577_CR5","doi-asserted-by":"publisher","first-page":"197","DOI":"10.2341\/12-158-L","volume":"38","author":"S Murat","year":"2013","unstructured":"Murat S, Kamburo\u01e7lu K, Isayev A, Kur\u015fun S, Y\u00fcksel S. Visibility of artificial buccal recurrent caries under restorations using different radiographic techniques. Oper Dent. 2013;38(2):197\u2013207.","journal-title":"Oper Dent"},{"issue":"8","key":"5577_CR6","doi-asserted-by":"publisher","first-page":"924","DOI":"10.1016\/j.jdent.2015.02.009","volume":"43","author":"F Schwendicke","year":"2015","unstructured":"Schwendicke F, Tzschoppe M, Paris S. Radiographic caries detection: a systematic review and meta-analysis. J Dent. 2015;43(8):924\u201333.","journal-title":"J Dent"},{"issue":"10","key":"5577_CR7","doi-asserted-by":"publisher","first-page":"794","DOI":"10.4329\/wjr.v6.i10.794","volume":"6","author":"N Shah","year":"2014","unstructured":"Shah N, Bansal N, Logani A. Recent advances in imaging technologies in dentistry. World J Radiol. 2014;6(10):794.","journal-title":"World J Radiol"},{"key":"5577_CR8","doi-asserted-by":"crossref","unstructured":"Schulze R, Heil U, Gross D, Bruellmann DD, Dranischnikow E, Schwanecke U, et al. Artefacts in CBCT: a review. DentomaxillofacRadiol. 2011;40(5):265\u201373.","DOI":"10.1259\/dmfr\/30642039"},{"key":"5577_CR9","doi-asserted-by":"crossref","unstructured":"Erickson BJ, Korfiatis P, Akkus Z, Kline TL. Machine learning for medical imaging. Radiographics2017; 37(2): 505\u201315.","DOI":"10.1148\/rg.2017160130"},{"key":"5577_CR10","first-page":"904103","volume":"9041","author":"A Cruz-Roa","year":"2014","unstructured":"Cruz-Roa A, Basavanhally A, Gonz\u00e1lez F, Gilmore H, Feldman M, Ganesan S, et al. Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks. Med Imaging. 2014;9041:904103.","journal-title":"Med Imaging"},{"key":"5577_CR11","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, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, et al. A survey on deep learning in medical image analysis. Med Image Anal. 2017;42:60\u201388.","journal-title":"Med Image Anal"},{"issue":"5","key":"5577_CR12","doi-asserted-by":"publisher","first-page":"1047","DOI":"10.3390\/diagnostics12051047","volume":"12","author":"X Lin","year":"2022","unstructured":"Lin X, Hong D, Zhang D, Huang M, Yu H. Detecting proximal caries on periapical radiographs using convolutional neural networks with different training strategies on small datasets. Diagnostics. 2022;12(5):1047.","journal-title":"Diagnostics"},{"key":"5577_CR13","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1016\/j.jdent.2018.07.015","volume":"77","author":"JH Lee","year":"2018","unstructured":"Lee JH, Kim DH, Jeong SN, Choi SH. Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm. J Dent. 2018;77:106\u201311.","journal-title":"J Dent"},{"issue":"1","key":"5577_CR14","doi-asserted-by":"publisher","first-page":"623","DOI":"10.1007\/s00784-021-04040-1","volume":"26","author":"Y Bayraktar","year":"2022","unstructured":"Bayraktar Y, Ayan E. Diagnosis of interproximal caries lesions with deep convolutional neural network in digital bitewing radiographs. Clin Oral Investig. 2022;26(1):623\u201332.","journal-title":"Clin Oral Investig"},{"key":"5577_CR15","doi-asserted-by":"publisher","first-page":"103425","DOI":"10.1016\/j.jdent.2020.103425","volume":"100","author":"AG Cantu","year":"2020","unstructured":"Cantu AG, Gehrung S, Krois J, Chaurasia A, Rossi JG, Gaudin R, et al. Detecting caries lesions of radiographic extension on bitewings using deep learning. J Dent. 2020;100:103425.","journal-title":"J Dent"},{"issue":"13","key":"5577_CR16","doi-asserted-by":"publisher","first-page":"4613","DOI":"10.3390\/s21134613","volume":"21","author":"YC Mao","year":"2021","unstructured":"Mao YC, Chen TY, Chou HS, Lin SY, Liu SY, Chen YA, et al. Caries and restoration detection using bitewing film based on transfer learning with CNNs. Sensors. 2021;21(13):4613.","journal-title":"Sensors"},{"key":"5577_CR17","doi-asserted-by":"publisher","first-page":"16807","DOI":"10.1038\/s41598-021-96368-7","volume":"11","author":"S Lee","year":"2021","unstructured":"Lee S, Oh SI, Jo J, Kang S, Shin Y, Park JW. Deep learning for early dental caries detection in bitewing radiographs. Sci Rep. 2021;11:16807.","journal-title":"Sci Rep"},{"issue":"3","key":"5577_CR18","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1007\/s00784-024-05566-w","volume":"28","author":"B Ayhan","year":"2024","unstructured":"Ayhan B, Ayan E, Bayraktar Y. A novel deep learning-based perspective for tooth numbering and caries detection. Clin Oral Investig. 2024;28(3):178.","journal-title":"Clin Oral Investig"},{"issue":"5","key":"5577_CR19","doi-asserted-by":"publisher","first-page":"2005","DOI":"10.3390\/app11052005","volume":"11","author":"TH Bui","year":"2021","unstructured":"Bui TH, Hamamoto K, Paing MP. Deep fusion feature extraction for caries detection on dental panoramic radiographs. Appl Sci. 2021;11(5):2005.","journal-title":"Appl Sci"},{"issue":"9","key":"5577_CR20","doi-asserted-by":"publisher","first-page":"1672","DOI":"10.3390\/diagnostics11091672","volume":"11","author":"L Lian","year":"2021","unstructured":"Lian L, Zhu T, Zhu F, Zhu H. Deep learning for caries detection and classification. Diagnostics. 2021;11(9):1672.","journal-title":"Diagnostics"},{"key":"5577_CR21","doi-asserted-by":"publisher","first-page":"104115","DOI":"10.1016\/j.jdent.2022.104115","volume":"122","author":"H Mohammad-Rahimi","year":"2022","unstructured":"Mohammad-Rahimi H, Motamedian SR, Rohban MH, Krois J, Uribe SE, Mahmoudinia E, et al. Deep learning for caries detection: a systematic review. J Dent. 2022;122:104115.","journal-title":"J Dent"},{"key":"5577_CR22","doi-asserted-by":"publisher","unstructured":"Redmon J, Divvala S, Girshick R, Farhadi A. You only look once: Unified, real-time object detection. Proc IEEE Conf Comput Vis Pattern Recognit (CVPR). 2016;779\u2013788. https:\/\/doi.org\/10.1109\/CVPR.2016.91.","DOI":"10.1109\/CVPR.2016.91"},{"issue":"4","key":"5577_CR23","doi-asserted-by":"publisher","first-page":"929","DOI":"10.3390\/electronics12040929","volume":"12","author":"L Cao","year":"2023","unstructured":"Cao L, Zheng X, Fang L. The semantic segmentation of standing tree images based on the Yolo V7 deep learning algorithm. Electronics. 2023;12(4):929.","journal-title":"Electronics"},{"issue":"13","key":"5577_CR24","doi-asserted-by":"publisher","first-page":"1452","DOI":"10.1177\/00220345211013808","volume":"100","author":"CM M\u00f6rch","year":"2021","unstructured":"M\u00f6rch CM, Atsu S, Cai W, Li X, Madathil SA, Liu X, et al. Artificial intelligence and ethics in dentistry: a scoping review. J Dent Res. 2021;100(13):1452\u201360.","journal-title":"J Dent Res"},{"key":"5577_CR25","doi-asserted-by":"crossref","unstructured":"Altan B, Gunec HG, Cinar S, Kutal S, Gulum S, Aydin KC. Detecting prosthetic restorations using artificial intelligence on panoramic radiographs. Sci Program. 2022; 2022: 1\u20136.","DOI":"10.1155\/2022\/6384905"},{"key":"5577_CR26","doi-asserted-by":"publisher","unstructured":"Liu C, Tao Y, Liang J, Li K, Chen Y. Object detection based on YOLO network. In 2018 IEEE 4th information technology and mechatronics engineering conference (ITOEC). 2018; 799\u2013803 https:\/\/doi.org\/10.1109\/ITOEC.2018.8740604","DOI":"10.1109\/ITOEC.2018.8740604"},{"issue":"10","key":"5577_CR27","doi-asserted-by":"publisher","first-page":"1659","DOI":"10.3390\/agriculture12101659","volume":"12","author":"K Jiang","year":"2022","unstructured":"Jiang K, Xie T, Yan R, Wen X, Li D, Jiang H, et al. An attention mechanism-improved YOLOv7 object detection algorithm for hemp duck count estimation. Agriculture. 2022;12(10):1659.","journal-title":"Agriculture"},{"issue":"3","key":"5577_CR28","doi-asserted-by":"publisher","first-page":"677","DOI":"10.3390\/jmse11030677","volume":"11","author":"K Liu","year":"2023","unstructured":"Liu K, Sun Q, Sun D, Peng L, Yang M, Wang N. Underwater target detection based on improved YOLOv7. J Mar Sci Eng. 2023;11(3):677.","journal-title":"J Mar Sci Eng"},{"issue":"4","key":"5577_CR29","doi-asserted-by":"publisher","first-page":"1801","DOI":"10.3390\/s23041801","volume":"23","author":"Y Zhang","year":"2023","unstructured":"Zhang Y, Sun Y, Wang Z, Jiang Y. YOLOv7-RAR for urban vehicle detection. Sensors. 2023;23(4):1801.","journal-title":"Sensors"},{"key":"5577_CR30","first-page":"40","volume":"8693","author":"TY Lin","year":"2014","unstructured":"Lin TY, Maire M, Belongie S, Hays J, Perona P, Ramanan D. Doll\u00e1rP,Zitnick CL: Microsoft COCO: common objects in context. ECCV. 2014;8693:40\u201355. Lecture Notes in Computer Science.","journal-title":"ECCV"},{"issue":"2","key":"5577_CR31","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","volume":"88","author":"M Everingham","year":"2010","unstructured":"Everingham M, Van Gool L, Williams I, Winn CK, Zisserman J. The Pascal visual object classes (VOC) challenge. Int J Comput Vis. 2010;88(2):303\u201338.","journal-title":"Int J Comput Vis"},{"key":"5577_CR32","doi-asserted-by":"publisher","unstructured":"Padilla R, Netto SL, da Silva. EAB.A survey on performance metrics for object-detection algorithms. 2020 International Conference on Systems, Signals and Image Processing (IWSSIP) 2020; 237\u2013242 https:\/\/doi.org\/10.1109\/IWSSIP48289.2020.9145130","DOI":"10.1109\/IWSSIP48289.2020.9145130"},{"issue":"6","key":"5577_CR33","first-page":"2635","volume":"36","author":"W Panyarak","year":"2023","unstructured":"Panyarak W, Wantanajittikul K, Charuakkra A, Prapayasatok S, Suttapak W. Enhancing. Caries Detection in bitewing radiographs using YOLOv7. J Digit. 2023;36(6):2635\u201347.","journal-title":"J Digit"},{"issue":"3","key":"5577_CR34","doi-asserted-by":"publisher","first-page":"363","DOI":"10.1007\/s11282-021-00572-0","volume":"38","author":"M Ba\u015faran","year":"2022","unstructured":"Ba\u015faran M, \u00c7elik \u00d6, Bayrakdar IS, et al. Diagnostic charting of panoramic radiography using deep-learning artificial intelligence system. Oral Radiol. 2022;38(3):363\u20139.","journal-title":"Oral Radiol"},{"issue":"8","key":"5577_CR35","first-page":"911","volume":"10","author":"I Chen","year":"2023","unstructured":"Chen I, Yang C, Chen M, Weng R, Yeh C. Deep learning-based Recognition of Periodontitis and Dental Caries in Dental X-ray images. Bioeng. 2023;10(8):911.","journal-title":"Bioeng"}],"container-title":["BMC Oral Health"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12903-025-05577-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12903-025-05577-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12903-025-05577-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,10]],"date-time":"2025-02-10T11:21:20Z","timestamp":1739186480000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcoralhealth.biomedcentral.com\/articles\/10.1186\/s12903-025-05577-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,10]]},"references-count":35,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["5577"],"URL":"https:\/\/doi.org\/10.1186\/s12903-025-05577-3","relation":{},"ISSN":["1472-6831"],"issn-type":[{"value":"1472-6831","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,10]]},"assertion":[{"value":"26 November 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 January 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 February 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":"This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Non-Interventional Research Ethics Committee of the K\u0131r\u0131kkale University (Date: 28 September 2022, Decision No: 2022.09.22). There was no need for individual consent, and the need for informed consent was waived by the Non-Interventional Research Ethics Committee of the K\u0131r\u0131kkale University for this retrospective study because the data and patient details were anonymized.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"Not applicable.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Clinical trial number"}}],"article-number":"216"}}