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

The study introduces a deep learning-based computer vision tool for high throughput quantification of individual hair fibers, capable of distinguishing and extracting overlapping fibers to measure features like length, width, and color. This tool was used to analyze the effects of hormone signaling, genetic modifications, and aging on hair follicle output in mice. The findings highlight distinct hair phenotypes associated with endocrinological, developmental, and aging-related changes, demonstrating the tool's potential for characterizing factors that influence hair follicles and developing new diagnostic methods for disease detection through hair analysis. An interactive web tool for exploring the data is available at skinregeneration.org.