Academic Journal
Predicting Perceived Profile Attractiveness From Cephalometric Measurements Using Machine Learning.
| Τίτλος: | Predicting Perceived Profile Attractiveness From Cephalometric Measurements Using Machine Learning. |
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| Συγγραφείς: | Pirayesh Z; Department of Orthodontics and Dentofacial Orthopedics, School of Dentistry, Zanjan University of Medical Sciences, Zanjan, Iran; Topic Group Oral Health, ITU/WHO/WIPO Global Initiative on Artificial Intelligence for Health, Geneva, Switzerland., Sohrabniya F; Topic Group Oral Health, ITU/WHO/WIPO Global Initiative on Artificial Intelligence for Health, Geneva, Switzerland; Department of Dentistry and Oral Health, Aarhus University, Aarhus, Denmark., Ourang SA; Department of Orthodontics, School of Dentistry, Dentofacial Deformities Research Center, Research Institute of Dental Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran., Mohammad-Rahimi H; Topic Group Oral Health, ITU/WHO/WIPO Global Initiative on Artificial Intelligence for Health, Geneva, Switzerland; Department of Dentistry and Oral Health, Aarhus University, Aarhus, Denmark; Department of Conservative Dentistry, Periodontology and Digital Dentistry, LMU University Hospital, Munich, Germany., Esmaeili S; Department of Orthodontics, School of Dentistry, Dentofacial Deformities Research Center, Research Institute of Dental Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran., Khosravani S; Department of Orthodontics, School of Dentistry, Dentofacial Deformities Research Center, Research Institute of Dental Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran., Motamedian SR; Department of Orthodontics, School of Dentistry, Dentofacial Deformities Research Center, Research Institute of Dental Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran. Electronic address: drmotamedian@gmail.com., Schwendicke F; Topic Group Oral Health, ITU/WHO/WIPO Global Initiative on Artificial Intelligence for Health, Geneva, Switzerland; Department of Conservative Dentistry, Periodontology and Digital Dentistry, LMU University Hospital, Munich, Germany., Tichy A; Topic Group Oral Health, ITU/WHO/WIPO Global Initiative on Artificial Intelligence for Health, Geneva, Switzerland; Department of Conservative Dentistry, Periodontology and Digital Dentistry, LMU University Hospital, Munich, Germany; Institute of Dental Medicine, First Faculty of Medicine, Charles University, Prague, Czech Republic. |
| Πηγή: | International dental journal [Int Dent J] 2026 Aug; Vol. 76 (4), pp. 109679. Date of Electronic Publication: 2026 Jun 24. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Elsevier Country of Publication: England NLM ID: 0374714 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1875-595X (Electronic) Linking ISSN: 00206539 NLM ISO Abbreviation: Int Dent J Subsets: MEDLINE |
| Imprint Name(s): | Publication: 2021- : London : Elsevier Original Publication: London : FDI World Dental |
| Ιατρικοί όροι (MeSH): | Cephalometry*/methods , Face*/anatomy & histology , Machine Learning* , Esthetics, Dental* , Beauty*, Humans ; Female ; Male ; Adolescent ; Predictive Learning Models ; Random Forest ; Neural Networks, Computer ; Adult ; Prediction Algorithms ; Boosting Machine Learning Algorithms ; Young Adult ; Soft Computing |
| Περίληψη: | Objectives: Orthodontic treatment planning requires careful consideration of facial aesthetics. This study aimed to compare machine learning (ML) models to predict perceived attractiveness based on lateral cephalometric measurements and to identify parameters that significantly impact perceived attractiveness. Methods: Lateral cephalometric radiographs and corresponding facial photographs of 400 patients were utilized in this study. Twenty-six raters, including 20 laypeople and 6 domain experts (art, maxillofacial surgery, and orthodontics), evaluated each photograph using a 1 to 5 Visual Analogue Scale (VAS), and cephalometric analyses using the Dolphin software served as the reference standard. Five ML models, linear regression, Support Vector Machine, XGBoost Regressor, Random Forest, and artificial neural network, were trained to predict attractiveness scores based on the reference standard, and their performance on a test set (n = 59) was evaluated using root mean square error (RMSE). Subsequently, feature importance analysis was conducted to identify cephalometric indices that had the largest impact on VAS scores. Results: Experts consistently rated facial attractiveness higher than laypeople, especially in age groups >15 years. Furthermore, female raters rated female profiles significantly higher and male profiles significantly lower than male raters, with differences evident in >15 age groups. Among the 5 ML models, Random Forest demonstrated the best performance (RMSE = 0.50). The feature importance analysis revealed that soft tissue parameters, especially those related to facial convexity, maxillary and mandibular prognathism, and vertical proportions, were most predictive of attractiveness scores. Conclusion: ML models showed promising results in predicting facial attractiveness based on cephalometric measurements and highlighted the correlation of facial aesthetics with soft tissue parameters. The proposed ML-based approach can aid clinicians in the objective assessment of treatment outcomes. (Copyright © 2026 The Authors. Published by Elsevier Inc. All rights reserved.) |
| Competing Interests: | Conflict of interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Falk Schwendicke is cofounder of dentalxrai, a company focusing on AI-based analytics in dentistry. The current work was fully independent of this activity. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. |
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| Contributed Indexing: | Keywords: Aesthetics; Artificial intelligence; Cephalometric analysis; Orthodontics |
| Entry Date(s): | Date Created: 20260624 Date Completed: 20260707 Latest Revision: 20260813 |
| Update Code: | 20260813 |
| PubMed Central ID: | PMC13319888 |
| DOI: | 10.1016/j.identj.2026.109679 |
| PMID: | 42341597 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 1875-595X |
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| DOI: | 10.1016/j.identj.2026.109679 |