Automated Quantification of Shedding Hairs from Smartphone Photographs Using an AI Tool for Alopecia Monitoring.

INTRODUCTION: Objective shed-hair counts can support clinical assessment, longitudinal monitoring, and evaluation of treatment response. Manual counting is time-consuming, whereas visual estimates vary between observers. We developed an artificial intelligence (AI) model to count shedding hairs from ordinary smartphone photographs. METHODS: Thirty volunteers acquired 5630 home smartphone photographs of dark shedding hairs on light backgrounds, and paired manual counts served as the reference standard. Images were assigned by participant into training/internal validation (24 volunteers, 4509 images) and independent testing (6 volunteers, 1121 images). A weakly supervised density-regression model was trained with image-level counts. A blinded reader study compared AI with visual estimates from 15 readers across 56 paired image sets. A non-randomized sequential refined wash test (RWT) comparison evaluated completion and usable quantitative data in historical manual and subsequent AI-assisted cohorts. RESULTS: In independent testing, AI achieved a mean absolute error (MAE) of 5.4 hairs and a mean absolute percentage error (MAPE) of 7.0%. The mean model-minus-reference bias was -0.8 hairs (95% LoA, -17.0 to 15.4 hairs). In the blinded comparison, AI had lower error than all visual estimation methods (MAE, 6.99 hairs; MAPE, 6.15%; P < 0.001). Median total AI workflow time was 24.5 seconds versus 123.0 seconds for manual counting. Usable RWT data were available for 14 of 48 patients (29.2%) in the historical manual cohort and 23 of 30 (76.7%) in the subsequent AI-assisted cohort (P < 0.001). CONCLUSION: Under the evaluated dark-hair and light-background conditions, AI provided low-error counts and lower error than human visual estimation. The subsequent AI-assisted RWT cohort more often provided usable quantitative data than the historical manual cohort in this non-randomized sequential comparison. Prospective studies are needed to evaluate workflow feasibility and potential benefits for clinical monitoring and patient outcomes.