发表机构
Faculty of Engineering, Cairo University; Dermatology Department, Faculty of Medicine, Cairo University（开罗大学工程学院; 开罗大学医学院皮肤病学系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出一种结合毛囊定位、计数、宽度估计与可解释规则层的多阶段AI框架，用于低倍率头皮图像评估雄激素性脱发，实验验证其高精度，旨在辅助皮肤科医生诊断。
AI 中文摘要
雄激素性脱发（AGA）的特征是模式化的毛囊微小化、单毛发毛囊单位增加以及毛干直径改变。我们提出了一种自动化的定量头皮分析和临床决策支持框架，该框架结合了毛囊单位（FU）定位、有序可见毛干计数、校准的毛干宽度估计、区域聚合以及可解释的规则层。临床队列包括243名患者（127名AGA，116名非AGA），而计算机视觉实验使用了160名专家标注的患者、2,400张毛发光镜图像和约158,000个FU标注。在患者不相交评估下，YOLOv8m实现了测试mAP@0.5=0.920和召回率=0.860；带有支持图通道的EfficientNet-B5实现了87.0%的专家框计数准确率（宏F1=0.85）。一个单独的500张图像集使用检测器生成的框进行端到端处理，相对于人类专家标注，毛囊检测的MAE为6.56，毛囊分类的MAE为16.59。该系统旨在辅助而非取代皮肤科医生的解读。
英文摘要
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.
