Enhanced Stratification of Male Pattern Hair Loss Using AI Through Novel Loss Region Ratio Analysis

This study introduces an AI-based framework for grading male pattern hair loss (MPHL) using a novel area ratio metric, which provides a more objective and standardized assessment compared to traditional methods. Analyzing 761 images from 257 patients, the AI model achieved high accuracy, with an average precision of 97.6% in bounding box evaluations and 96.1% in mask assessments. The area ratio metric outperformed traditional classifications, particularly in higher MPHL grades, enhancing diagnostic precision and consistency. Despite challenges in distinguishing intermediate stages and limitations in dataset diversity, the study highlights the potential of AI to improve MPHL diagnostics and support personalized treatments, with future research suggested to expand the dataset and refine algorithms.