BaldPredict: AI-Driven Prediction Model for Alopecia Areata

August 2025

Mandeep Singh, Prabhjot Kaur, Rinku Garg

alopecia areata machine learning autoimmune disease hair loss random forest support vector machines k-nearest network convolutional neural networks AA ML SVM KNN CNN

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.

Automatically generated from the study's abstract, not written by a person, and not a review of the full paper. Not medical advice or a treatment recommendation. Read the original study, and consult a qualified healthcare professional before changing treatment. Full disclaimer

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research Classification Framework for Healthy Hair and Alopecia Areata: A Machine Learning Approach

8 citations , August 2021 in “Computational and Mathematical Methods in Medicine”

This article proposes a machine learning framework for classifying healthy hair and alopecia areata using image processing and classification techniques, but does not report new clinical findings.

research Asian Hair: A Review of Structures, Properties, and Distinctive Disorders

39 citations , April 2020 in “Clinical, Cosmetic and Investigational Dermatology”

This review discusses the unique characteristics and disorders of Asian hair, emphasizing the need for more comprehensive studies in this area.

research Pathomechanisms of Immune-Mediated Alopecia

45 citations , April 2019 in “International Immunology”

This review discusses the mechanisms behind alopecia areata and primary cicatricial alopecia and reports no new clinical results, aiming to guide the development of targeted treatments.

research Alopecia Areata: A Multifactorial Autoimmune Condition

148 citations , December 2018 in “Journal of autoimmunity”

This review discusses genetic and environmental factors contributing to autoimmunity in alopecia areata and reports no new clinical findings, emphasizing the need for further study on its aetiology and pathophysiology.

research Quantifying Alopecia Areata via Texture Analysis to Automate the SALT Score Computation

20 citations , December 2017 in “Journal of Investigative Dermatology Symposium Proceedings”

This article presents a computer imaging algorithm that may automate and enhance the Severity of Alopecia Tool scoring for alopecia areata through texture analysis of pediatric images.

research New-Generation Therapies for the Treatment of Hair Loss in Men

15 citations , October 2017 in “Dermatologic Clinics”

This abstract discusses advancements in hair transplantation technology, highlighting the role of robotics and follicular unit extraction/grafting in achieving excellent clinical outcomes, with adjuvant treatments like platelet-rich plasma, lasers, and stem cells potentially improving the durability and appearance of results.

research Trichoscopic Clues for Diagnosis of Alopecia Areata and Trichotillomania in Asians

37 citations , January 2017 in “International Journal of Dermatology”

This study found that trichoscopic features like exclamation mark hairs and angulated hairs were more indicative of alopecia areata in an Asian population, while features like broken hairs of different lengths and flame hairs were more common in trichotillomania.

research Alopecia Areata: A Review

29 citations , June 2013 in “Journal of the Saudi Society of Dermatology & Dermatologic Surgery”

This review outlines the mechanisms, clinical features, and management strategies for alopecia areata and presents no new experimental findings.

research TrichoScan: A Novel Tool for the Analysis of Hair Growth In Vivo

70 citations , June 2003 in “Journal of Investigative Dermatology Symposium Proceedings”

This study reports that the TrichoScan method effectively measures hair growth parameters and detected significant improvements in hair counts and thickness in men with androgenetic alopecia after finasteride treatment.