Innovative Protocol for Consistent Facial Photography in Aesthetic Surgery: A Cost-Effective Solution

Area of Science:

• Medical imaging
• Plastic surgery
• Machine learning applications

Background:

• Standardized facial photography is essential for consistent documentation in plastic surgery and medical aesthetics.
• Existing methods struggle with patient variability, equipment, and environmental factors, leading to inconsistencies.
• This study introduces a cost-effective photographic method enhanced by machine learning for improved reproducibility and precision.

Purpose of the Study:

• To develop and validate a standardized photographic system minimizing angular deviations (yaw, pitch, roll).
• To test the hypothesis that the method achieves high reproducibility and consistency across different photographers and time points.

Main Methods:

• Photographed participants (n=20) in 5 standard positions using a specialized setup with a rotational chair and fixed lighting.
• Utilized a pretrained machine learning model to quantify head angles from images.
• Performed statistical analyses including Shapiro-Wilk and Hotelling's T-squared tests for normality and reproducibility.

Main Results:

• Mean yaw angle differences were within ±2° across all positions.
• Pitch and roll values remained near zero, indicating minimal deviation.
• Hotelling's T-squared test showed no significant differences between photo sessions (P=.7626), confirming high reproducibility.

Conclusions:

• A simple, reliable photographic method for consistent facial imaging in clinical practice was developed.
• Machine learning integration enhances accuracy and validates the standardization approach.
• The method is suitable for resource-limited settings, meeting high medical imaging standards.