Digital-Twin-Enabled IoMT System for Surgical Simulation Using rAC-GAN

A digital-twin (DT)-enabled Internet of Medical Things (IoMT) system for telemedical simulation is developed, systematically integrated with mixed reality (MR), 5G cloud computing, and a generative adversarial network (GAN) to achieve remote lung cancer implementation. Patient-specific data from 90 pulmonary embolism (PE)-positive patients, 1372 control groups, were gathered Qujing Dehong, then transmitted preprocessed using 5G. novel robust auxiliary classifier GAN (rAC-GAN)-based intelligent employed facilitate the PE prediction model. To improve accuracy immersion during surgical implementation, real-time operating room perspective perception layer navigation image projected surgeon’s helmet in application DT-based MR guide clue The accuracies area under curve (AUC) our new IoMT 0.92 0.93. Furthermore, pathogenic features learned rAC-GAN model are highly consistent statistical epidemiological results. proposed generates significant performance improvement process substantial clinical at centers shows framework medical transfer deep learning analytics