AI system detects hair loss from scalp images with dermatologist help
An AI-Based Multi-Stage Approach for Androgenetic Alopecia Assessment from Low-Magnification Scalp Images
Computer Vision and Pattern Recognition
Summary
Hair loss called androgenetic alopecia changes the size and number of hair follicles on the scalp. The authors designed an AI system to automatically find and measure hair follicles in scalp pictures and help doctors decide if someone has this condition. They tested their system on hundreds of patients and thousands of images, showing it works similarly to human experts. The system is meant to support doctors rather than replace them.
What this means in practice
- •For dermatology clinics: Assist dermatologists by automatically analyzing scalp images to identify hair follicle characteristics indicating androgenetic alopecia.
- •For medical device developers: Integrate automated follicle detection and classification into scalp imaging devices to improve hair loss diagnosis workflows.$Commercial implications: Enables development of advanced scalp imaging products offering AI-powered hair loss assessment for clinical use.
Authors
Mahmoud Raslan, Nada Omar, Omar Khaled, Tarek Waleed, Mohamed Hazem, Rania Mounir, Solwan Elsamanoudy, Ahmed Mourad, Noura Adel, Muhammad Rushdi
Abstract
Androgenetic alopecia (AGA) is characterized by patterned follicular miniaturization, increased single-hair follicular units, and altered hair-shaft diameter. We present an automated quantitative scalp-analysis and clinical decision-support framework combining FU localization, ordinal visible-shaft counting, calibrated shaft-width estimation, regional aggregation, and an interpretable rule layer. The clinical cohort comprised 243 patients (127 AGA, 116 non-AGA), while the computer-vision experiments used 160 expert-annotated patients, 2,400 trichoscopic images, and approximately 158,000 FU annotations. Under patientdisjoint evaluation, YOLOv8m achieved test mAP@0.5=0.920 and recall=0.860; EfficientNet-B5 with a support-map channel achieved 87.0% expert-box count accuracy (macro F1=0.85). A separate 500-image set was processed end-to-end with detector-generated boxes, yielding MAE of 6.56 for follicle detection and 16.59 for follicle classification relative to human-expert annotations. The system is intended to assist, rather than replace, dermatologist interpretation.