2 citations
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January 2024 in “Journal of Emerging Investigators” In this study, researchers evaluated deep learning methods for diagnosing Alopecia Areata and found that a modified Inception-Resnet-v2 model achieved a high validation accuracy of 97.94% and loss of 10.4%, suggesting it as an effective tool for classifying alopecia-affected hair.
8 citations
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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.
January 2022 in “Journal of Pharmaceutical Negative Results” This study found that a VGG-SVM model using machine learning techniques achieved 98.31% accuracy in distinguishing alopecia areata from healthy hair based on image datasets.
8 citations
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November 2022 in “International Journal of Cosmetic Science” This review discusses the limitations of current hair classification systems and highlights the need for standardized reporting of key hair characteristics to improve study comparisons; it reports no new results.
March 2026 in “Frontiers in Medicine” This study suggests that traditional classification systems for pattern hair loss, while useful in the past, have limitations in accuracy and reproducibility, and highlights the potential of integrating digital imaging and AI to create more precise and biologically informed classification frameworks.
