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The challenge on forestalling monkeypox (Mpox) spread is the timely,convenient and accurate diagnosis for earlystage infected individuals. Here, wepropose a remote and realtime online visualization strategy, called "SuperMonitoring" to construct a low cost, convenient, timely and unspecializeddiagnosis of early-stage Mpox. Such AI-mediated "Super Monitoring" (Mpox-AISM)invokes a framework assembled by deep learning, data augmentation andself-supervised learning, as well as professionally classifies four subtypesaccording to dataset characteristics and evolution trend of Mpox and sevenother types of dermatopathya with high similarity, hence these featurestogether with reasonable program interface and threshold setting ensure thatits Recall (Sensitivity) was beyond 95.9% and the specificity was almost 100%.As a result, with the help of cloud service on Internet and communicationterminal, this strategy can be potentially utilized for the real-time detectionof earlystage Mpox in various scenarios including entry-exit inspection inairport, family doctor, rural area in underdeveloped region and wild toeffectively shorten the window period of Mpox spread.

Mpox-AISM: AI-Mediated Super Monitoring for Forestalling Monkeypox Spread