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.
6 citations
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August 2022 in “Molecules” This study indicates potential differences in the hair spectra of individuals with mood disorders, suggesting a promising, yet preliminary, method for noninvasive prediagnosis.
16 citations
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July 2016 in “Veterinary Dermatology” This study found that dermoscopy can identify distinctive features in canine pattern alopecia, such as hair shaft thinning and pigmentation patterns, which may aid in diagnosing this and potentially other skin disorders.
16 citations
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December 2016 in “ecancermedicalscience” This study suggests that ATR-FT-IR spectral analysis of hair fibres may detect breast cancer through increased lipid content changes linked to cancer presence.
October 2021 in “bioRxiv (Cold Spring Harbor Laboratory)” This study introduces the Hair Cell Analysis Toolbox (HCAT), a machine-learning software that automates the analysis of cochlear hair cells, enabling unbiased and comprehensive imaging data interpretation.