Mpox-AISM：面向猴痘及类猴痘疾病的AI介导超级监测
Mpox-AISM: AI-Mediated Super Monitoring for Mpox and Like-Mpox
AI总结：
本文提出AI介导的超级监测框架mpox-AISM，整合深度学习、数据增强、自监督学习和云服务，实现对早期猴痘及类猴痘皮肤疾病的实时准确诊断，诊断猴痘F1分数达96.6%。
摘要：
对早期猴痘（mpox）患者进行快速、准确的诊断对于避免其传播至关重要。然而，常见皮肤疾病与猴痘之间的相似性以及专业诊断的需求不可避免地妨碍了对早期猴痘患者的诊断，并助长了猴痘疫情的暴发。为应对这一挑战，我们提出了“超级监测”（Super Monitoring），这是一种利用人工智能（AI）和互联网技术对早期猴痘进行廉价、便捷、快速诊断的实时可视化技术。具体而言，AI介导的“超级监测”（mpox-AISM）整合了深度学习模型、数据增强、自监督学习和云服务。根据公开可获取的数据集，mpox-AISM在诊断猴痘时的精确率、召回率、特异性和F1分数分别达到99.3%、94.1%、99.9%和96.6%，并且在诊断猴痘、六种类猴痘皮肤疾病和正常皮肤方面实现了94.51%的准确率。借助互联网和通信终端，mpox-AISM有潜力在真实场景中对早期猴痘进行实时、准确的诊断，从而防止猴痘疫情暴发。
英文摘要：
Swift and accurate diagnosis for earlier-stage monkeypox (mpox) patients is crucial to avoiding its spread. However, the similarities between common skin disorders and mpox and the need for professional diagnosis unavoidably impaired the diagnosis of earlier-stage mpox patients and contributed to mpox outbreak. To address the challenge, we proposed "Super Monitoring", a real-time visualization technique employing artificial intelligence (AI) and Internet technology to diagnose earlier-stage mpox cheaply, conveniently, and quickly. Concretely, AI-mediated "Super Monitoring" (mpox-AISM) integrates deep learning models, data augmentation, self-supervised learning, and cloud services. According to publicly accessible datasets, mpox-AISM's Precision, Recall, Specificity, and F1-score in diagnosing mpox reach 99.3%, 94.1%, 99.9%, and 96.6%, respectively, and it achieves 94.51% accuracy in diagnosing mpox, six like-mpox skin disorders, and normal skin. With the Internet and communication terminal, mpox-AISM has the potential to perform real-time and accurate diagnosis for earlier-stage mpox in real-world scenarios, thereby preventing mpox outbreak.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
