AI皮肤影像工具可离线检测猴痘，准确率达96%。
Mpox Screen Lite: AI-Driven On-Device Offline Mpox Screening for Low-Resource African Mpox Emergency Response
- 用YOLOv8n模型分析皮肤图像，支持离线运行。
- 对猴痘检测达97%灵敏度，96%特异性，准确率96%。
- 适合非洲等医疗资源匮乏地区快速筛查使用。
2024年猴痘疫情在非洲尤为严重，尤其出现1b克隆株，暴露出资源匮乏地区诊断能力的显著短板。本研究开发并验证了一种基于人工智能（AI）的、可在本地设备上离线运行的猴痘筛查工具。采用基于YOLOv8n的深度学习模型，在2,700张图像（每类900张：猴痘、其他皮肤病、正常皮肤）上训练，包含合成数据。在360张图像上进行内部验证，540张图像测试，另在1,500张独立外部图像上进行大规模验证。性能指标包括准确率、精确率、召回率、F1分数、敏感性和特异性。最终测试集准确率达96%；猴痘检测中精确率为93%，召回率为97%，F1分数为95%；敏感性与特异性分别为97%和96%。外部验证结果一致，证实模型具有鲁棒性与泛化能力。该工具为资源有限地区提供快速、精准且可扩展的猴痘筛查方案，其离线功能与跨数据集高表现，预示其在缺乏传统诊断基础设施地区的监测与管理中具有重大应用潜力。
原文摘要 · Abstract (English)
Background: The 2024 Mpox outbreak, particularly severe in Africa with clade 1b emergence, has highlighted critical gaps in diagnostic capabilities in resource-limited settings. This study aimed to develop and validate an artificial intelligence (AI)-driven, on-device screening tool for Mpox, designed to function offline in low-resource environments. Methods: We developed a YOLOv8n-based deep learning model trained on 2,700 images (900 each of Mpox, other skin conditions, and normal skin), including synthetic data. The model was validated on 360 images and tested on 540 images. A larger external validation was conducted using 1,500 independent images. Performance metrics included accuracy, precision, recall, F1-score, sensitivity, and specificity. Findings: The model demonstrated high accuracy (96%) in the final test set. For Mpox detection, it achieved 93% precision, 97% recall, and an F1-score of 95%. Sensitivity and specificity for Mpox detection were 97% and 96%, respectively. Performance remained consistent in the larger external validation, confirming the model's robustness and generalizability. Interpretation: This AI-driven screening tool offers a rapid, accurate, and scalable solution for Mpox detection in resource-constrained settings. Its offline functionality and high performance across diverse datasets suggest significant potential for improving Mpox surveillance and management, particularly in areas lacking traditional diagnostic infrastructure.
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