Digital Twin-enabled IoMT System for Surgical Simulation using rAC-GAN


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

Publication DOI:
Divisions: College of Business and Social Sciences > Aston Business School
College of Business and Social Sciences > Aston Business School > Operations & Information Management
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Uncontrolled Keywords: Biomedical imaging,Digital Twin,Digital twins,IoMT,Lung cancer,Medical diagnostic imaging,Medical services,Mixed Reality.,Mixed reality,Remote Surgery,Robust Auxiliary Classifier Generative Adversarial Network (rAC-GAN) model,Surgery,Signal Processing,Information Systems,Hardware and Architecture,Computer Science Applications,Computer Networks and Communications
Publication ISSN: 2327-4662
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Related URLs: https://ieeexpl ... ocument/9778207 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2022-11-01
Published Online Date: 2022-05-19
Accepted Date: 2022-05-01
Authors: Tai, Yonghang
Zhang, Liqiang
Li, Qiong
Zhu, Chunsheng
Chang, Victor (ORCID Profile 0000-0002-8012-5852)
Rodrigues, Joel J. P. C.
Guizani, Mohsen



Version: Accepted Version

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