{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T15:05:24Z","timestamp":1791644724563,"version":"4.3.4"},"reference-count":36,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T00:00:00Z","timestamp":1772496000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T00:00:00Z","timestamp":1772496000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Orthod Craniofacial Res"],"published-print":{"date-parts":[[2026,8]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>To develop a deep learning\u2010based framework to automate sector classification of unerupted maxillary canines (UMCs), assessing its accuracy and reliability compared to human ones.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Material and Methods<\/jats:title>\n                    <jats:p>One thousand five hundred twenty\u2010eight UMCs from digital panoramic radiographs (PRs) were selected using data from the Dental Department, \u201cSan Giovanni di Dio e Ruggi d'Aragona\u201d Hospital, Salerno. After a training session with an expert in sector classification, six dental practitioners allocated UMCs of a 20% random sample of the original set (T0) in 3 different sectors according to Kim's sector classification system. The assessment was repeated after 4\u2009weeks (T1). The accuracy and reliability of the human in determining the position of the canines were defined based on the level of agreement between the trainer and the examiners and the intra\u2010examiner agreement, respectively, both assessed through Cohen's K. The same radiographs were tested on different artificial intelligence (AI) models, pre\u2010trained on the extended dataset. The best\u2010performing model was identified based on its sensitivity and precision, and the model accuracy and repeatability were determined.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Regarding UMC allocation according to different sectors, the agreement between examiners and trainer was 0.78 (95% confidence interval\u2009=\u20090.77\u20130.80). The overall intra\u2010examiner agreement was 0.85 (95% confidence interval\u2009=\u20090.83\u20130.87). DenseNet121 proved to be the best\u2010performing model in allocating UMCs in the three different sectors, with an overall accuracy and repeatability of 76.8% and 95.3%, respectively.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The developed framework provides an automated approach in sector classification of UMCs, whose accuracy is comparable to that of humans, but the reliability is greater.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/ocr.70106","type":"journal-article","created":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T17:02:24Z","timestamp":1772989344000},"page":"631-639","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Sector Classification of Unerupted Maxillary Canines: A Deep Learning\u2010Based Automated Framework Using Panoramic Radiographs"],"prefix":"10.1111","volume":"29","author":[{"given":"Marzio","family":"Galdi","sequence":"first","affiliation":[{"name":"Department of Medicine, Surgery and Dentistry University of Salerno  Baronissi SA Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3578-017X","authenticated-orcid":false,"given":"Davide","family":"Cannat\u00e0","sequence":"additional","affiliation":[{"name":"Department of Medicine, Surgery and Dentistry University of Salerno  Baronissi SA Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Flavia","family":"Celentano","sequence":"additional","affiliation":[{"name":"Department of Medicine, Surgery and Dentistry University of Salerno  Baronissi SA Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luigia","family":"Rizzo","sequence":"additional","affiliation":[{"name":"Department of Computer Science University of Salerno  Fisciano SA 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