{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T15:21:12Z","timestamp":1791300072380,"version":"4.1.0"},"reference-count":22,"publisher":"Oxford University Press (OUP)","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Dentomaxillofacial Radiology"],"published-print":{"date-parts":[[2023,9]]},"abstract":"<jats:sec><jats:title>Objectives:<\/jats:title><jats:p> The aim of the study was to evaluate the efficacy of traditional supervised learning (SL) and semi-supervised learning (SSL) in the classification of mandibular third molars (Mn3s) on panoramic images. The simplicity of preprocessing step and the outcome of the performance of SL and SSL were analyzed. <\/jats:p><\/jats:sec><jats:sec><jats:title>Methods:<\/jats:title><jats:p> Total 1625 Mn3s cropped images from 1000 panoramic images were labeled for classifications of the depth of impaction (D class), spatial relation with adjacent second molar (S class), and relationship with inferior alveolar nerve canal (N class). For the SL model, WideResNet (WRN) was applicated and for the SSL model, LaplaceNet (LN) was utilized. <\/jats:p><\/jats:sec><jats:sec><jats:title>Results:<\/jats:title><jats:p> In the WRN model, 300 labeled images for D and S classes, and 360 labeled images for N class were used for training and validation. In the LN model, only 40 labeled images for D, S, and N classes were used for learning. The F1 score were 0.87, 0.87, and 0.83 in WRN model, 0.84, 0.94, and 0.80 for D class, S class, and N class in the LN model, respectively. <\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions:<\/jats:title><jats:p> These results confirmed that the LN model applied as SSL, even utilizing a small number of labeled images, demonstrated the satisfactory of the prediction accuracy similar to that of the WRN model as SL. <\/jats:p><\/jats:sec>","DOI":"10.1259\/dmfr.20230030","type":"journal-article","created":{"date-parts":[[2023,5,16]],"date-time":"2023-05-16T17:35:03Z","timestamp":1684258503000},"source":"Crossref","is-referenced-by-count":15,"title":["The efficacy of supervised learning and semi-supervised learning in diagnosis of impacted third molar on panoramic radiographs through artificial intelligence model"],"prefix":"10.1093","volume":"52","author":[{"given":"Ji-Youn","family":"Kim","sequence":"first","affiliation":[{"name":"Division of Oral & Maxillofacial Surgery, Department of Dentistry, St. Vincent\u2019s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Se Hoon","family":"Kahm","sequence":"additional","affiliation":[{"name":"Department of Dentistry, Eunpyeong St. Mary\u2019s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seok","family":"Yoo","sequence":"additional","affiliation":[{"name":"AI Business Headquarters, Unidocs Inc., Seoul, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Soo-Mi","family":"Bae","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence, Graduate school, Korea University, Seoul, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ji-Eun","family":"Kang","sequence":"additional","affiliation":[{"name":"JINHAKapply Corp., Seoul, South 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