Mpox-AISM: AI-Mediated Super Monitoring for Mpox and Like-Mpox
This addresses the problem of rapid and accurate mpox diagnosis for public health to prevent outbreaks, though it appears incremental as it combines existing AI techniques.
The paper tackled the challenge of diagnosing early-stage monkeypox (mpox) by proposing an AI-based 'Super Monitoring' system that integrates deep learning and cloud services, achieving up to 99.9% specificity and 94.51% accuracy in distinguishing mpox from similar skin disorders.
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.