2 citations
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January 2020 in “International Journal of Medical Sciences” This study found that several factors, including ponytail hairstyles and alcohol consumption, were associated with increased severity of female pattern hair loss in the surveyed Chinese women.
37 citations
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October 2015 in “European Journal of Human Genetics” This study found that a genetic model using SNPs can predict early-onset male-pattern baldness with moderate accuracy, which may assist in decisions about interventions.
The authors of this study developed a novel CNN architecture aimed at improving detection of Alopecia Areata through image-based datasets, achieving a top accuracy of 98% compared to four other machine learning models.
4 citations
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March 2021 in “Le infezioni in medicina” This study found that male COVID-19 patients with androgenic alopecia at a Peruvian hospital were more likely to experience moderate to severe symptoms compared to those without alopecia.
2 citations
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May 2025 in “Diagnostics” This study found that ATR-FTIR spectroscopy combined with machine learning effectively differentiated alopecia areata patients from healthy controls with an AUC of 0.85, and also showed promise in predicting treatment response, particularly through alterations in the Amide I band.