January 2015 in “Springer eBooks” Understanding hair structure and growth is key for diagnosing hair diseases accurately.
December 2019 in “Periodicals of Engineering and Natural Sciences (PEN)” This research reported that using J48 algorithms with bagging improves prediction accuracy of hair health through machine learning by analyzing factors like spatial-temporal images, gender, and age, achieving a real-time performance of 89.5%.
2 citations
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December 2023 in “Royal Society of Chemistry eBooks” The book discussed in this abstract is a comprehensive guide to hair analysis, detailing the latest methods and applications across various fields such as toxicology and drug monitoring, designed for both newcomers and experts in the field.
27 citations
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September 2012 in “Dermatologic Clinics” This review outlines a systematic method for diagnosing hair loss and presents no new findings; the authors emphasize the importance of biopsy in cicatricial alopecia cases.
January 2015 in “Springer eBooks” This chapter describes two simple, noninvasive methods — the pull test and modified wash test — which can diagnose and assess the severity of female hair loss, including conditions like telogen effluvium and androgenetic alopecia.