Hair Analysis Based on Medical History and Spatial-Temporal Data

This study presents a machine learning algorithm that achieved 89.5% accuracy in predicting hair health using factors like spatial-temporal images, age, and gender.

This study developed a comprehensive atlas of human scalp tissue to uncover cell states and lineages in hair follicles, identifying disease-specific cellular changes associated with non-scarring and scarring hair loss, which may offer new diagnostic and therapeutic insights.

This review discusses the potential of using human hair proteomics for non-invasive diagnostic and forensic applications but reports no new results; the authors highlight areas needing further research.

This review highlights recent innovations in hair specimen analysis, discussing its applications in medical diagnostics, forensic science, and stress assessment while noting challenges such as hair growth variability and contamination that limit its current impact.

Understanding hair structure and growth is key for diagnosing hair diseases accurately.