Purpose
This study developed and validated a deep learning model for the automated early detection of androgenetic alopecia (AGA) using trichoscopic images, and evaluated the model’s diagnostic performance in a Korean clinical cohort.
Methods
We conducted a retrospective observational study using 318 trichoscopic scalp images labeled by board-certified dermatologists according to the Basic and Specific (BASP) system, collected at Ewha Womans University Medical Center between July 2018 and January 2024. The images were categorized as BASP 0 (no hair loss) or BASP 1–3 (early-stage hair loss). A ResNet-18 convolutional neural network, pretrained on ImageNet, was fine-tuned for binary classification. Internal validation was performed using stratified 5-fold cross-validation, and external validation was conducted through ensemble soft voting on a separate hold-out test set of 20 images. Model performance was measured by accuracy, precision, recall, F1-score, and area under the curve (AUC), with 95% confidence intervals (CIs) calculated for hold-out accuracy.
Results
Internal validation revealed robust model performance, with 4 out of 5 folds achieving an accuracy above 0.90 and an AUC above 0.93. In external validation on the hold-out test set, the ensemble model achieved an accuracy of 0.90 (95% CI, 0.77–1.03) and an AUC of 0.97, with perfect recall for early-stage hair loss. No missing data were present, and the model demonstrated stable convergence without requiring data augmentation.
Conclusion
This model demonstrated high accuracy and generalizability for detecting early-stage AGA from trichoscopic images, supporting its potential utility as a screening tool in clinical and teledermatology settings.

Citations

• Trichoscopy and Computational Models for Hair and Scalp Disorders: Image Analysis, Quantification, and Clinical Integration
  Corrado Zengarini, Nico Curti, Stephano Cedirian, Luca Rapparini, Francesca Pampaloni, Alessandro Pileri, Francesco Durazzi, Martina Mussi, Michelangelo La Placa, Bianca Maria Piraccini, Michela Starace
  Applied Sciences.2026; 16(7): 3199.     CrossRef
• How Can Clinicians Leverage Vibe Coding for Machine Learning and Deep Learning Research?
  Yoonhwan Lee, Sun Huh
  Endocrinology and Metabolism.2025; 40(5): 659.     CrossRef