ArticleClinical, cosmetic and investigational dermatology2026
Automated Quantification of Shedding Hairs from Smartphone Photographs Using an AI Tool for Alopecia Monitoring.
Article in Clinical, cosmetic and investigational dermatology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.