Automating Hair Loss Labels for Universally Scoring Alopecia from Images

December 2022 in “ JAMA Dermatology ”

Cameron Gudobba, Tejas Mane, Aylar Bayramova, Natàlia Rodríguez, Leslie Castelo‐Soccio, Temitayo Ogunleye, Susan C. Taylor, George Cotsarelis, Elena Bernardis

alopecia HairComb convolutional neural network hair loss patterns hair density percentage hair loss dermatologists artificial intelligence algorithm AI CNN

Studysummary This study found that the HairComb algorithm achieved high accuracy in quantifying percentage hair loss across various types of alopecia, suggesting its potential for standardized automated assessments.

Our plain-language summary. Not medical advice or a treatment recommendation. Consult a qualified healthcare professional before changing treatment. Full disclaimer

The document discusses a study that developed an artificial intelligence system, HairComb, to automatically score alopecia severity from images. The system was trained on 1,000 images and tested on 200 images, showing a high level of agreement with scores given by dermatologists. The study involved 404 participants and the algorithm showed a 92% accuracy rate. HairComb uses a convolutional neural network to determine hair loss patterns and hair density, providing a percentage hair loss at every pixel. The algorithm was validated as a standalone tool for any alopecia subtype and showed strong agreement with manual scores. The study concludes that HairComb could be a useful tool in assessing alopecia severity, potentially improving diagnosis and treatment planning.

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