{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T15:05:23Z","timestamp":1791644723760,"version":"4.3.4"},"reference-count":34,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2025,6,9]],"date-time":"2025-06-09T00:00:00Z","timestamp":1749427200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Cultivation Program for Junior Talents of Nanjing Stomatological School, Medical School of Nanjing University","award":["0222E305"],"award-info":[{"award-number":["0222E305"]}]},{"name":"Cultivation Program for Junior Talents of Nanjing Stomatological School, Medical School of Nanjing University","award":["0224C044"],"award-info":[{"award-number":["0224C044"]}]},{"name":"High-Level Hospital Construction Project of Nanjing Stomatological Hospital, Affiliated Hospital of Medical School, Institute of Stomatology, Nanjing University","award":["0222E305"],"award-info":[{"award-number":["0222E305"]}]},{"name":"High-Level Hospital Construction Project of Nanjing Stomatological Hospital, Affiliated Hospital of Medical School, Institute of Stomatology, Nanjing University","award":["0224C044"],"award-info":[{"award-number":["0224C044"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Objective: The objective of this study is to develop a convolutional neural network (CNN) for the automatic detection of soft and hard tissue landmarks and the classification of lip thickness on lateral cephalometric radiographs. Methods: A dataset of 1019 pre-orthodontic lateral cephalograms from patients with diverse malocclusions was utilized. A CNN-based model was trained to automatically detect 22 cephalometric landmarks. Upper and lower lip thicknesses were measured using some of these landmarks, and a pre-trained decision tree model was employed to classify lip thickness into the thin, normal, and thick categories. Results: The mean radial error (MRE) for detecting 22 landmarks was 0.97 \u00b1 0.52 mm. Successful detection rates (SDRs) at threshold distances of 1.00, 1.50, 2.00, 2.50, 3.00, and 4.00 mm were 72.26%, 89.59%, 95.41%, 97.66%, 98.98%, and 99.47%, respectively. For nine soft tissue landmarks, the MRE was 1.08 \u00b1 0.87 mm. Lip thickness classification accuracy was 0.91 \u00b1 0.04 (upper lip) and 0.90 \u00b1 0.04 (lower lip) in females and 0.92 \u00b1 0.03 (upper lip) and 0.88 \u00b1 0.05 (lower lip) in males. The area under the curve (AUC) values for lip thickness were \u22650.97 for all gender\u2013lip combinations. Conclusions: The CNN-based landmark detection model demonstrated high precision, enabling reliable automatic classification of lip thickness using cephalometric radiographs.<\/jats:p>","DOI":"10.3390\/diagnostics15121468","type":"journal-article","created":{"date-parts":[[2025,6,10]],"date-time":"2025-06-10T03:54:18Z","timestamp":1749527658000},"page":"1468","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Automated Landmark Detection and Lip Thickness Classification Using a Convolutional Neural Network in Lateral Cephalometric Radiographs"],"prefix":"10.3390","volume":"15","author":[{"given":"Miaomiao","family":"Han","sequence":"first","affiliation":[{"name":"Department of Orthodontics, Nanjing Stomatological Hospital, Affiliated Hospital of Medical School, Research Institute of Stomatology, Nanjing University, Nanjing 210008, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhengqun","family":"Huo","sequence":"additional","affiliation":[{"name":"School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210003, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiangyan","family":"Ren","sequence":"additional","affiliation":[{"name":"Department of Orthodontics, Nanjing Stomatological Hospital, Affiliated Hospital of Medical School, Research Institute of Stomatology, Nanjing University, Nanjing 210008, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5840-2251","authenticated-orcid":false,"given":"Haiting","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210003, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huang","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Orthodontics, Nanjing Stomatological Hospital, Affiliated Hospital of Medical School, Research Institute of Stomatology, Nanjing University, Nanjing 210008, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jialing","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Orthodontics, Nanjing Stomatological Hospital, Affiliated Hospital of Medical School, Research Institute of Stomatology, Nanjing University, Nanjing 210008, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7856-8091","authenticated-orcid":false,"given":"Li","family":"Mei","sequence":"additional","affiliation":[{"name":"Discipline of Orthodontics, Department of Oral Science, Faculty of Dentistry, University of Otago, Dunedin 9016, New Zealand"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,9]]},"reference":[{"key":"ref_1","first-page":"284","article-title":"A concept of facial esthetics","volume":"40","author":"Peck","year":"1970","journal-title":"Angle Orthod."},{"key":"ref_2","first-page":"262","article-title":"Prediction of soft tissue profile changes following orthodontic retraction of incisors in Iranian girls","volume":"11","author":"Sodagar","year":"2010","journal-title":"World J. 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