ArticleInternational dental journal2026
Predicting Perceived Profile Attractiveness From Cephalometric Measurements Using Machine Learning.
Article in International dental journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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9 authors.
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Abstract
objectivesOrthodontic 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.
methodsLateral 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.
resultsExperts 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.
conclusionML 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.
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