{"slug": "an-ai-based-multi-stage-approach-for-androgenetic-alopecia-assessment-from-low", "title": "An AI-Based Multi-Stage Approach for Androgenetic Alopecia Assessment from Low-Magnification Scalp Images", "summary": "A multi-stage AI framework for assessing androgenetic alopecia (AGA) from low-magnification scalp images achieved a test mAP@0.5 of 0.920 and recall of 0.860 with YOLOv8m under patient-disjoint evaluation, according to an arXiv paper (arXiv:2610.02421v1). The system, trained on a clinical cohort of 243 patients (127 AGA, 116 non-AGA) and 2,400 trichoscopic images with roughly 158,000 follicular-unit annotations, also reached 87.0% expert-box count accuracy (macro F1=0.85) using EfficientNet-B5 with a support-map channel. End-to-end processing of a separate 500-image set with detector-generated boxes produced a mean absolute error of 6.56 for follicle detection and 16.59 for follicle classification versus human-expert annotations; the authors state the system is intended to assist, not replace, dermatologist interpretation.", "body_md": "arXiv:2610.02421v1 Announce Type: new \nAbstract: Androgenetic alopecia (AGA) is characterized by patterned follicular miniaturization, increased single-hair follicular units, and altered hair-shaft diameter. We present an automated quantitative scalp-analysis and clinical decision-support framework combining FU localization, ordinal visible-shaft counting, calibrated shaft-width estimation, regional aggregation, and an interpretable rule layer. The clinical cohort comprised 243 patients (127 AGA, 116 non-AGA), while the computer-vision experiments used 160 expert-annotated patients, 2,400 trichoscopic images, and approximately 158,000 FU annotations. Under patientdisjoint evaluation, YOLOv8m achieved test mAP@0.5=0.920 and recall=0.860; EfficientNet-B5 with a support-map channel achieved 87.0% expert-box count accuracy (macro F1=0.85). A separate 500-image set was processed end-to-end with detector-generated boxes, yielding MAE of 6.56 for follicle detection and 16.59 for follicle classification relative to human-expert annotations. The system is intended to assist, rather than replace, dermatologist interpretation.", "url": "https://wpnews.pro/news/an-ai-based-multi-stage-approach-for-androgenetic-alopecia-assessment-from-low", "canonical_source": "https://arxiv.org/abs/2610.02421", "published_at": "2026-10-05 04:00:00+00:00", "updated_at": "2026-10-05 04:13:32.982993+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "computer-vision", "ai-research"], "entities": ["arXiv", "YOLOv8m", "EfficientNet-B5", "androgenetic alopecia"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/an-ai-based-multi-stage-approach-for-androgenetic-alopecia-assessment-from-low", "markdown": "https://wpnews.pro/news/an-ai-based-multi-stage-approach-for-androgenetic-alopecia-assessment-from-low.md", "text": "https://wpnews.pro/news/an-ai-based-multi-stage-approach-for-androgenetic-alopecia-assessment-from-low.txt", "jsonld": "https://wpnews.pro/news/an-ai-based-multi-stage-approach-for-androgenetic-alopecia-assessment-from-low.jsonld"}}