{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T15:58:44Z","timestamp":1792598324203,"version":"4.5.0"},"reference-count":26,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2022,3,30]],"date-time":"2022-03-30T00:00:00Z","timestamp":1648598400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Int J Paed Dentistry"],"published-print":{"date-parts":[[2022,9]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Supernumerary teeth are a common anomaly and are frequently observed in paediatric patients. To prevent or minimize complications, early diagnosis and treatment is ideal in children with supernumerary teeth.<\/jats:p><\/jats:sec><jats:sec><jats:title>Aim<\/jats:title><jats:p>This study aimed to apply convolutional neural network (CNN)\u2013based deep learning to detect the presence of supernumerary teeth in children during the early mixed dentition stage.<\/jats:p><\/jats:sec><jats:sec><jats:title>Design<\/jats:title><jats:p>Three CNN models, AlexNet, VGG16\u2010TL, and InceptionV3\u2010TL, were employed in this study. A total of 220 panoramic radiographs (from children aged 6\u00a0years 0\u00a0months to 9\u00a0years 6\u00a0months) including supernumerary teeth (cases, <jats:italic>n<\/jats:italic>\u00a0=\u00a0120) or no anomalies (controls, <jats:italic>n<\/jats:italic>\u00a0=\u00a0100) were retrospectively analyzed. The CNN performances were assessed according to accuracy, sensitivity, specificity, receiver operating characteristic (ROC) curves, and area under the ROC curves for a cross\u2010validation test dataset.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The VGG16\u2010TL model had the highest performance according to accuracy, sensitivity, specificity, and area under the ROC curve, but the other models had similar performance.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>CNN\u2010based deep learning is a promising approach for detecting the presence of supernumerary teeth during the early mixed dentition stage.<\/jats:p><\/jats:sec>","DOI":"10.1111\/ipd.12946","type":"journal-article","created":{"date-parts":[[2021,12,14]],"date-time":"2021-12-14T07:31:27Z","timestamp":1639467087000},"page":"678-685","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":71,"title":["Detecting the presence of supernumerary teeth during the early mixed dentition stage using deep learning algorithms: A pilot study"],"prefix":"10.1111","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7057-1955","authenticated-orcid":false,"given":"Yuichi","family":"Mine","sequence":"first","affiliation":[{"name":"Department of Medical System Engineering Graduate School of Biomedical and Health Sciences Hiroshima University  Hiroshima Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9387-7746","authenticated-orcid":false,"given":"Yuko","family":"Iwamoto","sequence":"additional","affiliation":[{"name":"Department of Pediatric Dentistry Graduate School of Biomedical and Health Sciences Hiroshima University  Hiroshima Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shota","family":"Okazaki","sequence":"additional","affiliation":[{"name":"Department of Medical System Engineering Graduate School of Biomedical and Health Sciences Hiroshima University  Hiroshima Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kentaro","family":"Nakamura","sequence":"additional","affiliation":[{"name":"Department of Medical System Engineering Graduate School of Biomedical and Health Sciences Hiroshima University  Hiroshima Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saori","family":"Takeda","sequence":"additional","affiliation":[{"name":"Department of Medical System Engineering Graduate School of Biomedical and Health Sciences Hiroshima University  Hiroshima Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tzu\u2010Yu","family":"Peng","sequence":"additional","affiliation":[{"name":"Research Center of Digital Oral Science and Technology College of Oral Medicine Taipei Medical University  Taipei Taiwan"},{"name":"School of Dentistry College of Oral Medicine Taipei Medical University  Taipei Taiwan"},{"name":"Department of Anatomy and Functional 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