1 citations
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March 2024 in “Skin research and technology” In this study, a modified Xception deep learning model achieved a 92% accuracy rate in diagnosing hair and scalp disorders, significantly outperforming other models, suggesting AI could improve dermatological diagnostics' accuracy and accessibility.
4 citations
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January 2021 in “Dermatologic Therapy” This review highlights current and future AI applications in hair restoration and diagnosis of hair disorders, including automated systems for hair detection and self-diagnosis, emphasizing the need for experts to understand their benefits and limitations.
3 citations
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January 2019 in “Electronic Imaging” This study found that a lightweight Convolutional Neural Network model can accurately and quickly determine natural hair tone from high-resolution images of hair roots, outperforming other popular methods.
20 citations
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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.
44 citations
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November 2016 in “Journal of The American Academy of Dermatology” This article updates a diagnostic tool (SALT II) for assessing hair loss by adding more precise measurements of scalp surface area, particularly useful for various types of alopecia.