Automated Early Detection of Androgenetic Alopecia Using Deep Learning on Trichoscopic Images From a Korean Cohort: A Retrospective Model Development and Validation Study

A deep learning model accurately detects early hair loss signs using scalp images.

In this study, the BaldGraphFormer framework, integrating visual and clinical data, outperformed unimodal baselines in early-stage androgenetic alopecia detection, achieving an F1-score of 97.62% and macro-average AUC of 0.992, suggesting its potential to support dermatological decision-making and early intervention.

The Ewha Medical Journal is now in PubMed, has an AI article editor, and offers Korean reporting guidelines.

This review highlights current and future AI applications in hair restoration and diagnosis of hair disorders, including automated systems for hair detection and self-diagnosis, emphasizing the need for experts to understand their benefits and limitations.