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Predicting Perceived Profile Attractiveness From Cephalometric Measurements Using Machine Learning
Zeynab Pirayesh1, Fatemeh Sohrabniya2, Seyed AmirHossein Ourang3
1Department 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.
Machine learning models effectively predict facial attractiveness using cephalometric data. Soft tissue measurements, particularly facial convexity and jaw proportions, significantly influence perceived beauty, aiding orthodontic treatment planning.
Area of Science:
- Orthodontics
- Aesthetic Dentistry
- Machine Learning in Healthcare
Background:
- Facial aesthetics are crucial in orthodontic treatment planning.
- Objective assessment of attractiveness is challenging.
- Lateral cephalometric measurements offer quantifiable data for analysis.
Purpose of the Study:
- To compare machine learning (ML) models for predicting perceived facial attractiveness.
- To identify key cephalometric parameters influencing attractiveness scores.
- To evaluate the utility of ML in objective aesthetic assessment for orthodontics.
Main Methods:
- Utilized lateral cephalometric radiographs and facial photographs from 400 patients.
- Employed 26 raters (laypeople and experts) to score attractiveness via Visual Analogue Scale (VAS).
- Trained five ML models (Linear Regression, SVM, XGBoost, Random Forest, ANN) on cephalometric data, evaluating performance with RMSE.
Main Results:
- Random Forest model achieved the best performance (RMSE = 0.50).
- Soft tissue parameters, including facial convexity, maxillary/mandibular prognathism, and vertical proportions, were most predictive.
- Experts rated attractiveness higher than laypeople; rater gender influenced ratings in specific age groups.
Conclusions:
- ML models demonstrate potential for predicting facial attractiveness from cephalometric data.
- Facial aesthetics are significantly correlated with specific soft tissue cephalometric parameters.
- This ML-based approach can assist clinicians in objectively evaluating orthodontic treatment outcomes.
