A web-based artificial intelligence system for label-free virus classification and detection of cytopathic effects.

Identifying viral replication within cells demands labor-intensive isolation methods, requiring specialized personnel and additional confirmatory tests. To facilitate this process, we developed an AI-powered automated system called AI Recognition of Viral CPE (AIRVIC), specifically designed to detect and classify label-free cytopathic effects (CPEs) induced by SARS-CoV-2, BAdV-1, BPIV3, BoAHV-1, and two strains of BoGHV-4 in Vero and MDBK cell lines. AIRVIC utilizes convolutional neural networks, with ResNet50 as the primary architecture, trained on 40,369 microscopy images at various magnifications.