{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T20:38:56Z","timestamp":1786653536232,"version":"build-2736575974"},"reference-count":14,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"5","license":[{"start":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T00:00:00Z","timestamp":1777507200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Aim:<\/jats:title>\n                    <jats:p>The aim of this study was to develop and evaluate a deep learning-based model for the detection of occlusal caries in intraoral scans using YOLOv5.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods:<\/jats:title>\n                    <jats:p>Intraoral scans were obtained from 117 patients, yielding 330 carious teeth (moderate and extensive lesions). Teeth were annotated using the International Caries Detection and Assessment System framework. YOLOv5 variants were trained with different preprocessing modules (single-channel, mixed channel, Ohta transformation). Performance was assessed using precision, recall, F1-score, mean average precision, and validated against expert annotations using Cohen\u2019s kappa and McNemar\u2019s test.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>\n                      The best-performing YOLOv5 model achieved an accuracy of 90% in detecting moderate and extensive lesions. There was a significant amount of agreement with expert examiners (\n                      <jats:italic toggle=\"yes\">\u03ba<\/jats:italic>\n                      =0.77). No discernible difference (\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      = 1.0) between artificial intelligence (AI) predictions and human annotations according to McNemar\u2019s test was found.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion:<\/jats:title>\n                    <jats:p>YOLOv5-based detection of occlusal caries in intraoral scans demonstrated strong diagnostic performance, comparable to expert annotations. Targeted preprocessing further enhanced model reliability, underscoring the potential of intraoral scan-based AI diagnostics in conservative dentistry.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.4103\/jcde.jcde_1025_25","type":"journal-article","created":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T07:01:22Z","timestamp":1777532482000},"page":"497-502","update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":0,"title":["Artificial intelligence in dentistry: Training and testing a caries detection model with intraoral scans"],"prefix":"10.4103","volume":"29","author":[{"given":"Rudra","family":"Kaul","sequence":"first","affiliation":[{"name":"Department of Conservative Dentistry and Endodontics, IGGDC, Jammu, Jammu and Kashmir, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meghanshu","family":"Verma","sequence":"additional","affiliation":[{"name":"Post-Graduate, Department of Conservative Dentistry and Endodontics, Indira Gandhi Government Dental College and Hospital, Jammu, Jammu and Kashmir, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Himanshu","family":"Singh","sequence":"additional","affiliation":[{"name":"Post-Graduate, Department of Conservative Dentistry and Endodontics, Indira Gandhi Government Dental College and Hospital, Jammu, Jammu and Kashmir, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shamim","family":"Anjum","sequence":"additional","affiliation":[{"name":"Department of Conservative Dentistry and Endodontics, IGGDC, Jammu, Jammu and Kashmir, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seerat","family":"Sharma","sequence":"additional","affiliation":[{"name":"Department of Public Health Dentistry, Swami Devi Dyal Hospital and Dental College, Panchkula, Haryana, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"276","published-online":{"date-parts":[[2026,4,30]]},"reference":[{"key":"R1-20260504","doi-asserted-by":"crossref","first-page":"650","DOI":"10.1177\/0022034515573272","article-title":"Global burden of untreated caries:A systematic review and metaregression","volume":"94","author":"Kassebaum","year":"2015","journal-title":"J Dent Res"},{"key":"R2-20260504","doi-asserted-by":"crossref","first-page":"364","DOI":"10.4103\/0972-0707.164032","article-title":"Comparative clinical evaluation of the efficacy of a new method for caries diagnosis and excavation","volume":"18","author":"Peskersoy","year":"2015","journal-title":"J Conserv Dent"},{"key":"R3-20260504","doi-asserted-by":"crossref","first-page":"199","DOI":"10.4103\/jcd.jcd_575_22","article-title":"DIAGNOdent versus international caries detection and assessment system in detection of incipient carious lesions:A diagnostic accuracy study","volume":"26","author":"Shaalan","year":"2023","journal-title":"J Conserv Dent"},{"key":"R4-20260504","first-page":"1115","article-title":"Digital versus conventional workflows for the fabrication of fixed implant-supported restorations:A comparative clinical study","volume":"29","author":"Mangano","year":"2018","journal-title":"Clin Oral Implants Res"},{"key":"R5-20260504","doi-asserted-by":"crossref","first-page":"401","DOI":"10.4103\/JCDE.JCDE_172_25","article-title":"Revolutionizing the diagnosis of dental caries using artificial intelligence-based methods","volume":"28","author":"Marwaha","year":"2025","journal-title":"J Conserv Dent Endod"},{"key":"R6-20260504","doi-asserted-by":"crossref","first-page":"764","DOI":"10.4103\/JCDE.JCDE_362_25","article-title":"Artificial intelligence for dental caries detection:A mixup, fine-tuning, and quantization approach on the MobileNetV2 model","volume":"28","author":"Boy","year":"2025","journal-title":"J Conserv Dent Endod"},{"key":"R7-20260504","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1111\/j.1600-0528.2007.00347.x","article-title":"The international caries detection and assessment system (ICDAS):An integrated system for measuring dental caries","volume":"35","author":"Ismail","year":"2007","journal-title":"Community Dent Oral Epidemiol"},{"key":"R8-20260504","doi-asserted-by":"crossref","first-page":"889","DOI":"10.4103\/JCDE.JCDE_622_24","article-title":"Conquering the enemy within:Noncavitated carious lesions","volume":"27","author":"Singh","year":"2024","journal-title":"J Conserv Dent Endod"},{"key":"R10-20260504","doi-asserted-by":"crossref","first-page":"104821","DOI":"10.1016\/j.jdent.2023.104821","article-title":"AI-based dental caries and tooth number detection in intraoral photos:Model development and performance evaluation","volume":"141","author":"Yoon","year":"2024","journal-title":"J Dent"},{"key":"R11-20260504","doi-asserted-by":"crossref","first-page":"5351","DOI":"10.3390\/ijerph20075351","article-title":"Visual diagnostics of dental caries through deep learning of non-standardised photographs using a hybrid YOLO ensemble and transfer learning model","volume":"20","author":"Tareq","year":"2023","journal-title":"Int J Environ Res Public Health"},{"key":"R12-20260504","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1038\/s41746-023-00944-2","article-title":"Detection and localization of caries and hypomineralization on dental photographs with a vision transformer model","volume":"6","author":"Felsch","year":"2023","journal-title":"NPJ Digit Med"},{"key":"R13-20260504","doi-asserted-by":"crossref","first-page":"1731","DOI":"10.1007\/s00784-022-04801-6","article-title":"Assessment of YOLOv3 for caries detection in bitewing radiographs based on the ICCMS\u2122radiographic scoring system","volume":"27","author":"Panyarak","year":"2023","journal-title":"Clin Oral Investig"},{"key":"R14-20260504","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1016\/j.identj.2023.10.003","article-title":"Dental caries detection and classification in CBCT images using deep learning","volume":"74","author":"Esmaeilyfard","year":"2024","journal-title":"Int Dent J"},{"key":"R15-20260504","doi-asserted-by":"crossref","first-page":"104970","DOI":"10.1016\/j.jdent.2024.104970","article-title":"Performance comparison of multifarious deep networks on caries detection with tooth X-ray images","volume":"144","author":"Ying","year":"2024","journal-title":"J Dent"}],"container-title":["Journal of Conservative Dentistry and Endodontics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.ovid.com\/10.4103\/JCDE.JCDE_1025_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T19:39:02Z","timestamp":1786649942000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ovid.com\/10.4103\/JCDE.JCDE_1025_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,30]]},"references-count":14,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2026]]}},"URL":"https:\/\/doi.org\/10.4103\/jcde.jcde_1025_25","relation":{},"ISSN":["2950-4716","2950-4708"],"issn-type":[{"value":"2950-4716","type":"print"},{"value":"2950-4708","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,30]]},"assertion":[{"value":"2025-12-07","name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-03-11","name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}}]}}