{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T05:43:21Z","timestamp":1781588601466,"version":"3.54.5"},"reference-count":21,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T00:00:00Z","timestamp":1764115200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Journal of Prosthodontics"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To develop a deep\u2010learning system for identifying five dental implant brands from periapical radiographs and compare its diagnostic accuracy with dental professionals and evaluate successive You Only Look Once (YOLO) architectures (v7\u2013v12) to justify model selection.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>A dataset of 5851 periapical radiographs was compiled and divided into training, validation, and test partitions (80\/10\/10). After filtering to five brands (Adin, Dentium, Noris, OSSTEM, and Straumann), YOLO\u2010based object detection models (versions 7\u201312) were trained and tested under identical conditions. The YOLOv12x model was adopted for final evaluation based on its optimal balance of accuracy and inference speed. Human performance was assessed using 100 held\u2010out test images (20 per brand) via a multiple\u2010choice web survey distributed to six clinician groups. Diagnostic metrics included mean average precision at IoU 0.50 (mAP@50), mAP@50\u201395, precision, recall, and accuracy.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The model achieved an mAP@50 of 0.989 (98.9%), an mAP@50\u201395 of 0.900 (90.0%), a precision of 0.969 (96.9%), and a recall of 0.977 (97.7%) across brands. Across YOLO generations, performance improved from mAP@50\u201395 = 0.817 (YOLOv7) to 0.905 (YOLOv12x). Fifty\u2010two clinicians completed 5,200 image evaluations; the model significantly outperformed all clinician subgroups (one\u2010way ANOVA with Tukey HSD,\n                      <jats:italic>p<\/jats:italic>\n                      &lt; 0.001). Transformer\u2010based DF\u2010DETR achieved mAP@50\u201395 = 0.878, confirming YOLOv12x's superior efficiency\u2013accuracy trade\u2010off.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>A high\u2010performing model identified implant brands on periapical radiographs and outperformed clinicians across experience levels. Comparative analysis across YOLO architectures validated its measurable advantage in accuracy and speed. Lack of external validation and dataset imbalance are important limitations; future work will include external, multisite data and human\u2013AI workflow evaluation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/jopr.70064","type":"journal-article","created":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T06:35:49Z","timestamp":1764225349000},"page":"81-93","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Development and evaluation of an AI model for dental implant type detection: A comparison of diagnostic accuracy between a deep learning model and dental professionals"],"prefix":"10.1111","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1810-8733","authenticated-orcid":false,"given":"Walaa Magdy","family":"Ahmed","sequence":"first","affiliation":[{"name":"Department of Restorative Dentistry, Faculty of Dentistry King Abdulaziz University  Jeddah Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8749-4714","authenticated-orcid":false,"given":"Amr Ahmed","family":"Azhari","sequence":"additional","affiliation":[{"name":"Department of Restorative Dentistry, Faculty of Dentistry King Abdulaziz University  Jeddah Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdulrahman","family":"Almufti","sequence":"additional","affiliation":[{"name":"Master Prosthodontic Program, Faculty of Dentistry Riyadh Elm University  Riyadh Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zainab Majed","family":"Alsadah","sequence":"additional","affiliation":[{"name":"Dental Department East Jeddah General Hospital, Ministry of Health  Jeddah Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amr Fawzy Abdelhamid","family":"Ahmed","sequence":"additional","affiliation":[{"name":"Department Artificial Intelligence 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