{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T14:30:06Z","timestamp":1764253806580,"version":"3.46.0"},"reference-count":0,"publisher":"Biomedical Informatics","issue":"11","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioinformation"],"published-print":{"date-parts":[[2025,11,15]]},"abstract":"<jats:p>Accurate diagnosis of dental malocclusions remains challenging due to interobserver variability among orthodontists. Therefore, it is of interest to evaluate the diagnostic reliability of artificial intelligence (AI) algorithms in classifying malocclusion types using 3D dental models compared with expert orthodontist assessments. A convolutional neural network (CNN) was trained and tested on digital impressions, and its performance was statistically analyzed against expert diagnoses. Results demonstrated strong agreement between AI predictions and orthodontist evaluations with clinically relevant consistency. These findings highlight the potential of AI-assisted diagnostics to enhance accuracy and reduce subjectivity in orthodontic assessment.<\/jats:p>","DOI":"10.6026\/973206300214194","type":"journal-article","created":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T14:24:02Z","timestamp":1764253442000},"page":"4194-4197","source":"Crossref","is-referenced-by-count":0,"title":["AI for diagnosing malocclusions from 3D dental models"],"prefix":"10.6026","volume":"21","author":[{"name":"Department of Orthodontics, Cmai Medical Center, Uthman bin afan road, Al falah Riyadh, Saudi Arabia","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Imad","family":"Mohammed","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"4070","published-online":{"date-parts":[[2025,11,15]]},"container-title":["Bioinformation"],"original-title":[],"deposited":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T14:24:03Z","timestamp":1764253443000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.bioinformation.net\/021\/973206300214194.htm"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,15]]},"references-count":0,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2025,11,15]]},"published-print":{"date-parts":[[2025,11,15]]}},"URL":"https:\/\/doi.org\/10.6026\/973206300214194","relation":{},"ISSN":["0973-8894","0973-2063"],"issn-type":[{"type":"print","value":"0973-8894"},{"type":"electronic","value":"0973-2063"}],"subject":[],"published":{"date-parts":[[2025,11,15]]}}}