{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T11:49:36Z","timestamp":1790423376259,"version":"4.1.0"},"reference-count":26,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2021,3,25]],"date-time":"2021-03-25T00:00:00Z","timestamp":1616630400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2020R1A2C1003792"],"award-info":[{"award-number":["2020R1A2C1003792"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>The aim of this study was to reveal cranio-spinal differences between skeletal classification using convolutional neural networks (CNNs). Transverse and longitudinal cephalometric images of 832 patients were used for training and testing of CNNs (365 males and 467 females). Labeling was performed such that the jawbone was sufficiently masked, while the parts other than the jawbone were minimally masked. DenseNet was used as the feature extractor. Five random sampling crossvalidations were performed for two datasets. The average and maximum accuracy of the five crossvalidations were 90.43% and 92.54% for test 1 (evaluation of the entire posterior\u2013anterior (PA) and lateral cephalometric images) and 88.17% and 88.70% for test 2 (evaluation of the PA and lateral cephalometric images obscuring the mandible). In this study, we found that even when jawbones of class I (normal mandible), class II (retrognathism), and class III (prognathism) are masked, their identification is possible through deep learning applied only in the cranio-spinal area. This suggests that cranio-spinal differences between each class exist.<\/jats:p>","DOI":"10.3390\/diagnostics11040591","type":"journal-article","created":{"date-parts":[[2021,3,25]],"date-time":"2021-03-25T14:51:12Z","timestamp":1616683872000},"page":"591","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Deep-Learning-Based Detection of Cranio-Spinal Differences between Skeletal Classification Using Cephalometric Radiography"],"prefix":"10.3390","volume":"11","author":[{"given":"Seung Hyun","family":"Jeong","sequence":"first","affiliation":[{"name":"Safety System Research Group, Korea Institute of Industrial Technology (KITECH), Gyeongsan 38408, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jong Pil","family":"Yun","sequence":"additional","affiliation":[{"name":"Safety System Research Group, Korea Institute of Industrial Technology (KITECH), Gyeongsan 38408, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Han-Gyeol","family":"Yeom","sequence":"additional","affiliation":[{"name":"Department of Oral and Maxillofacial Radiology, Daejeon Dental Hospital, Wonkwang University College of Dentistry, Daejeon 35233, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hwi Kang","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Oral and Maxillofacial Surgery, Daejeon Dental Hospital, Wonkwang University College of Dentistry, Daejeon 35233, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2824-0439","authenticated-orcid":false,"given":"Bong Chul","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Oral and Maxillofacial Surgery, Daejeon Dental Hospital, Wonkwang University College of Dentistry, Daejeon 35233, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.aanat.2019.07.007","article-title":"Volumetric characteristics of prognathic mandible revealed by skeletal unit analysis","volume":"226","author":"Mun","year":"2019","journal-title":"Ann. 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