Deep Hair Phenomics: Implications in Endocrinology, Development, and Aging

The study presents a deep learning-based tool for analyzing hair phenotypes in mice, focusing on the effects of hormonal, genetic, and aging factors on hair composition. It highlights the tool's ability to detect subtle changes in hair features, such as length, width, and color, which are not visible to the naked eye. The research demonstrates the tool's accuracy in measuring and classifying hair types, revealing significant variability and a continuum of hair types rather than distinct categories. The study also examines the role of dermal LEF1 in hair follicle development, finding that its absence affects hair cycle progression and type proportions. The findings suggest that this technology could enhance understanding of hair biology and serve as a noninvasive diagnostic tool, with implications for both human and veterinary medicine.