BaldPredict: AI-Driven Prediction Model for Alopecia Areata

    August 2025
    Mandeep Singh, Prabhjot Kaur, Rinku Garg
    Studysummary In this study, machine learning-based computer-aided diagnosis significantly improved accuracy in diagnosing alopecia areata compared to traditional visual methods, achieving up to 91.9% accuracy using different classifiers like CNN, SVM, and random forest models.
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    Research cited in this study 9

    1. Classification Framework for Healthy Hair and Alopecia Areata: A Machine Learning Approach Computational and Mathematical Methods in Medicine · 2021
    2. Asian Hair: A Review of Structures, Properties, and Distinctive Disorders Clinical, Cosmetic and Investigational Dermatology · 2020
    3. Pathomechanisms of Immune-Mediated Alopecia International Immunology · 2019
    4. Alopecia Areata: A Multifactorial Autoimmune Condition Journal of autoimmunity · 2018
    5. Quantifying Alopecia Areata via Texture Analysis to Automate the SALT Score Computation Journal of Investigative Dermatology Symposium Proceedings · 2017
    6. New-Generation Therapies for the Treatment of Hair Loss in Men Dermatologic Clinics · 2017
    7. Trichoscopic Clues for Diagnosis of Alopecia Areata and Trichotillomania in Asians International Journal of Dermatology · 2017
    8. Alopecia Areata: A Review Journal of the Saudi Society of Dermatology & Dermatologic Surgery · 2013
    9. TrichoScan: A Novel Tool for the Analysis of Hair Growth In Vivo Journal of Investigative Dermatology Symposium Proceedings · 2003