A Machine Learning Algorithm Applied to Trichoscopy for Androgenic Alopecia Staging and Severity Assessment

    July 2023 in “ Dermatology practical & conceptual ”
    Marco Di Fraia, Lorenzo Tieghi, Francesca Magri … Alfredo Rossi
    Studysummary This study developed a support vector machine model using trichoscopic patterns to accurately classify androgenic alopecia severity, with an accuracy of 94.3% in training and 90.0% in test datasets.
    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
    Read the full study on dpcj.org →
    Discuss this study in the Community →

    Research cited in this study 6

    1. How Good Is Artificial Intelligence at Solving Hairy Problems? A Review of AI Applications in Hair Restoration and Hair Disorders Dermatologic Therapy · 2021
    2. Androgenetic Alopecia in Women and Men: Italian Guidelines Adapted from European Dermatology Forum/European Academy of Dermatology and Venereology Guidelines Giornale italiano di dermatologia e venereologia · 2020
    3. Female Androgenetic Alopecia: An Update on Diagnosis and Management American Journal of Clinical Dermatology · 2019
    4. Androgenetic Alopecia: A Review Endocrine · 2017
    5. Trichoscopic Findings of Androgenetic Alopecia and Their Association with Disease Severity Journal of Dermatology · 2015
    6. Women Who Present With Female Pattern Hair Loss Tend To Underestimate The Severity Of Their Hair Loss British Journal of Dermatology · 2004

    Related research 3

    1. Eruptive Vellus Hair Cysts: An Underdiagnosed Entity International journal of trichology · 2022
    2. The Main Cause of Hair Rarefaction in Androgenetic Alopecia: Higher Number and Longer Duration of Kenogen Hairs Skin appendage disorders · 2018
    3. Ambiguous Alopecia in a 36-Year-Old Woman 2012