{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T18:55:34Z","timestamp":1791658534272,"version":"4.3.4"},"reference-count":36,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T00:00:00Z","timestamp":1761091200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Background: Monocular Depth Estimation (MDE) is a computer vision approach that predicts spatial depth information from a single two-dimensional image. In orthodontics, where facial soft-tissue evaluation is integral to diagnosis and treatment planning, such methods offer new possibilities for obtaining sagittal profile information from standard frontal photographs. This study aimed to determine whether MDE can extract clinically meaningful information for facial profile assessment. Methods: Standardized frontal photographs and lateral cephalometric radiographs from 82 adult patients (48 Class I, 28 Class II, 6 Class III) were retrospectively analyzed. Three clinically relevant soft-tissue landmarks\u2014Upper Lip Anterior (ULA), Lower Lip Anterior (LLA), and Soft Tissue Pogonion (Pog\u2032)\u2014were annotated on frontal photographs, while true vertical line (TVL) analysis from cephalograms served as the reference standard. For each case, anteroposterior (AP) relationships among the three landmarks were represented as ordinal rankings based on predicted depth values, and accuracy was defined as complete agreement between model-derived and reference rankings. Depth maps were generated using one vision transformer model (DPT-Large) and two CNN-based models (DepthAnything-v2 and ZoeDepth). Model performance was evaluated using accuracy, 95% confidence intervals, and effect size measures. Results: The transformer-based DPT-Large achieved clinically acceptable accuracy in 92.7% of cases (76\/82; 95% CI: 84.8\u201397.3), significantly outperforming the CNN-based models DepthAnything-v2 (9.8%) and ZoeDepth (4.9%), both of which performed below the theoretical chance level (16.7%). Conclusions: Vision transformer-based Monocular Depth Estimation demonstrates the potential for clinically meaningful soft-tissue profiling from frontal photographs, suggesting that depth information derived from two-dimensional images may serve as a supportive tool for facial profile evaluation. These findings provide a foundation for future studies exploring the integration of depth-based analysis into digital orthodontic diagnostics.<\/jats:p>","DOI":"10.3390\/s25216512","type":"journal-article","created":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T05:20:44Z","timestamp":1761196844000},"page":"6512","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Camera-Based Monocular Depth Estimation in Orthodontics: Vision Transformer vs. CNN Model Performance"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-8920-8605","authenticated-orcid":false,"given":"Arda","family":"Ar\u0131san","sequence":"first","affiliation":[{"name":"Independent Researcher, Ankara 06000, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6152-6178","authenticated-orcid":false,"given":"G\u00f6khan Serhat","family":"Duran","sequence":"additional","affiliation":[{"name":"Department of Orthodontics, Faculty of Dentistry, \u00c7anakkale Onsekiz Mart University, \u00c7anakkale 17100, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"9","DOI":"10.18231\/j.ijrimcr.2025.003","article-title":"Artificial Intelligence and Its Application in Orthodontics: A Scoping Review","volume":"7","author":"Chauhan","year":"2025","journal-title":"Int. J. Recent Innov. Med. Clin. Res."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Kazimierczak, N., Kazimierczak, W., Serafin, Z., Nowicki, P., No\u017cewski, J., and Janiszewska-Olszowska, J. (2024). AI in Orthodontics: Revolutionizing Diagnostics and Treatment Planning\u2014A Comprehensive Review. J. Clin. Med., 13.","DOI":"10.3390\/jcm13020344"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Cenzato, N., Nobili, A., and Maspero, C. (2021). Prevalence of Dental Malocclusions in Different Geographical Areas: Scoping Review. Dent. J., 9.","DOI":"10.3390\/dj9100117"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"914","DOI":"10.1016\/j.ajodo.2015.09.007","article-title":"History of Imaging in Orthodontics from Broadbent to Cone-Beam Computed Tomography","volume":"148","author":"Hans","year":"2015","journal-title":"Am. J. Orthod. Dentofac. Orthop."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Liu, J., Zhang, C., Shan, Z., Liu, J., Zhang, C., and Shan, Z. (2023). Application of Artificial Intelligence in Orthodontics: Current State and Future Perspectives. Healthcare, 11.","DOI":"10.3390\/healthcare11202760"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/S0889-5406(97)70242-7","article-title":"Cephalometric Landmarks Identification and Reproducibility: A Meta Analysis","volume":"112","author":"Trpkova","year":"1997","journal-title":"Am. J. Orthod. Dentofac. Orthop."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Singh, P., Bornstein, M.M., Hsung, R.T.C., Ajmera, D.H., Leung, Y.Y., and Gu, M. (2024). Frontiers in Three-Dimensional Surface Imaging Systems for 3D Face Acquisition in Craniofacial Research and Practice: An Updated Literature Review. Diagnostics, 14.","DOI":"10.3390\/diagnostics14040423"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.ajodo.2021.02.013","article-title":"Machine Learning and Orthodontics, Current Trends and the Future Opportunities: A Scoping Review","volume":"160","author":"Nadimi","year":"2021","journal-title":"Am. J. Orthod. Dentofac. Orthop."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"97","DOI":"10.34172\/joddd.025.42109","article-title":"Smartphone-Based 3D Facial Scanning Applications in Orthodontic Diagnosis: A Cross-Sectional Study","volume":"19","author":"Nehru","year":"2025","journal-title":"J. Dent. Res. Dent. Clin. Dent. Prospect."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"e59839","DOI":"10.2196\/59839","article-title":"The Depth Estimation and Visualization of Dermatological Lesions: Development and Usability Study","volume":"7","author":"Parekh","year":"2024","journal-title":"JMIR Dermatol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2351004","DOI":"10.1142\/S0218001423510047","article-title":"CWD2GAN: Generative Adversarial Network of Chronic Wound Depth Detection for Predicting Chronic Wound Depth","volume":"37","author":"Chin","year":"2023","journal-title":"Int. J. Pattern Recognit. Artif. Intell."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"247","DOI":"10.20517\/ais.2024.17","article-title":"Long-Term Reprojection Loss for Self-Supervised Monocular Depth Estimation in Endoscopic Surgery","volume":"4","author":"Shi","year":"2024","journal-title":"Art. Int. Surg."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Chong, N., Yang, F., and Wei, K. (2025). A Monocular Endoscopic Image Depth Estimation Method Based on a Window-Adaptive Asymmetric Dual-Branch Siamese Network. Sci. Rep., 15.","DOI":"10.1038\/s41598-025-96804-y"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1177\/1553350613475883","article-title":"Stereophotogrammetry-Based Facial Depth Measurements: A Novel Method for Quantifying Facial Projection","volume":"21","author":"Jayaratne","year":"2014","journal-title":"Surg. Innov."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Xiao, Y., Mao, B., Nie, J., Liu, J., Wang, S., Liu, D., and Zhou, Y. (2024). Accuracy Evaluation of a Three-Dimensional Face Reconstruction Model Based on the Hifi3D Face Model and Clinical Two-Dimensional Images. Bioengineering, 11.","DOI":"10.3390\/bioengineering11121174"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"De Ridder, L., Aleksieva, A., Willems, G., Declerck, D., and de Llano-P\u00e9rula, M.C. (2022). Prevalence of Orthodontic Malocclusions in Healthy Children and Adolescents: A Systematic Review. Int. J. Environ. Res. Public Health, 19.","DOI":"10.3390\/ijerph19127446"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/S0889-5406(99)70234-9","article-title":"Soft Tissue Cephalometric Analysis: Diagnosis and Treatment Planning of Dentofacial Deformity","volume":"116","author":"Arnett","year":"1999","journal-title":"Am. J. Orthod. Dentofac. Orthop."},{"key":"ref_18","first-page":"231","article-title":"True Vertical Validation in Facial Orthognathic Surgery Planning","volume":"5","year":"2013","journal-title":"J. Clin. Exp. Dent."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"655","DOI":"10.1093\/ejo\/24.6.655","article-title":"The Reproducibility of Cephalometric Measurements: A Comparison of Analogue and Digital Methods","volume":"24","author":"Ongkosuwito","year":"2002","journal-title":"Eur. J. Orthod."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"382","DOI":"10.1016\/0002-9416(83)90322-6","article-title":"The Analysis of Errors in Orthodontic Measurements","volume":"83","author":"Houston","year":"1983","journal-title":"Am. J. Orthod."},{"key":"ref_21","unstructured":"Gulve, P., and Gupta, G. (2022). Monocular Depth Estimation from Single Image, Stanford University."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Khan, F., Salahuddin, S., and Javidnia, H. (2020). Deep Learning-Based Monocular Depth Estimation Methods\u2014A State-of-the-Art Review. Sensors, 20.","DOI":"10.3390\/s20082272"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1186\/1746-160X-10-54","article-title":"Facial Landmark Localization by Curvature Maps and Profile Analysis","volume":"10","author":"Lippold","year":"2014","journal-title":"Head. Face Med."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"cjaf009","DOI":"10.1093\/ejo\/cjaf009","article-title":"The Accuracy of Automated Facial Landmarking\u2014A Comparative Study between Cliniface Software and Patch-Based Convoluted Neural Network Algorithm","volume":"47","author":"Ju","year":"2025","journal-title":"Eur. J. Orthod."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Ranftl, R., Bochkovskiy, A., and Koltun, V. (2021, January 10\u201317). Vision Transformers for Dense Prediction. Proceedings of the IEEE International Conference on Computer Vision, Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.01196"},{"key":"ref_26","unstructured":"Bhat, S.F., Birkl, R., Wofk, D., Wonka, I.P., and M\u00fcller, M. (2023). ZoeDepth: Zero-Shot Transfer by Combining Relative and Metric Depth. arXiv."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Jia, J., Kang, J., Chen, L., Gao, X., Zhang, B., and Yang, G. (2025). A Comprehensive Evaluation of Monocular Depth Estimation Methods in Low-Altitude Forest Environment. Remote Sens., 17.","DOI":"10.3390\/rs17040717"},{"key":"ref_28","unstructured":"Rajpurkar, P., Hannun, A.Y., Haghpanahi, M., Bourn, C., and Ng, A.Y. (2017). Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"973","DOI":"10.1136\/heartjnl-2020-318686","article-title":"Machine Learning with Convolutional Neural Networks for Clinical Cardiologists","volume":"108","author":"Howard","year":"2022","journal-title":"Heart"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1959","DOI":"10.1007\/s11548-018-1860-1","article-title":"Deep Learning with Convolutional Neural Network for Objective Skill Evaluation in Robot-Assisted Surgery","volume":"13","author":"Wang","year":"2018","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1217","DOI":"10.1007\/s11548-019-01995-1","article-title":"Video-Based Surgical Skill Assessment Using 3D Convolutional Neural Networks","volume":"14","author":"Funke","year":"2019","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Boostani, M., B\u00e1nv\u00f6lgyi, A., Zouboulis, C.C., Goldfarb, N., Suppa, M., Goldust, M., L\u0151rincz, K., Kiss, T., N\u00e1dudvari, N., and Holl\u00f3, P. (2025). Large Language Models in Evaluating Hidradenitis Suppurativa from Clinical Images. J. Eur. Acad. Dermatol. Venereol.","DOI":"10.1111\/jdv.20861"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1881","DOI":"10.1111\/ijd.17729","article-title":"Diagnostic Performance of GPT-4o and Gemini Flash 2.0 in Acne and Rosacea","volume":"64","author":"Boostani","year":"2025","journal-title":"Int. J. Dermatol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"105056","DOI":"10.1016\/j.jdent.2024.105056","article-title":"Automatic Cephalometric Landmark Identification with Artificial Intelligence: An Umbrella Review of Systematic Reviews","volume":"146","author":"Polizzi","year":"2024","journal-title":"J. Dent."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Lee, J.H., Yu, H.J., Kim, M.J., Kim, J.W., and Choi, J. (2020). Automated Cephalometric Landmark Detection with Confidence Regions Using Bayesian Convolutional Neural Networks. BMC Oral Health, 20.","DOI":"10.1186\/s12903-020-01256-7"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1038\/nature21056","article-title":"Dermatologist-Level Classification of Skin Cancer with Deep Neural Networks","volume":"542","author":"Esteva","year":"2017","journal-title":"Nature"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/21\/6512\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T05:23:36Z","timestamp":1761197016000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/21\/6512"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,22]]},"references-count":36,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2025,11]]}},"alternative-id":["s25216512"],"URL":"https:\/\/doi.org\/10.3390\/s25216512","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,22]]}}}