Digital Twin Technology for Intelligent Medical Device Engineering

Abstract

Continuous advancements in medical device technologies require developing comprehensive frameworks for real-time monitoring, predictive maintenance, and adaptive control of medical devices to ensure patient safety and reliability of devices. Conventional ways of testing and simulation usually require static models, provide insufficient feedback in a timely manner, and do not consider the individual differences of patients; therefore, the performance of the device is less than optimal or the device fails. This chapter seeks to develop a comprehensive methodology for incorporating digital twin (DT) technology into the engineering process for intelligent medical devices within the medical device unified development process framework, which aims to connect virtual modeling and actual operation. High-fidelity system models were developed based on computer-aided design (CAD) and finite element analysis (FEA) models, connecting physical prototypes with these models using Internet of Things (IoT) devices and cloud computing. Artificial intelligence (AI) predictive models were implemented to optimize the device performance in real time, such as Long Short-Term Memory (LSTM), physics-informed neural networks (PINNs), Bayesian neural networks, and reinforcement learning. Across 10 state-of-the-art DT approaches, the quantitative results obtained normalized root mean square error (RMSE) values of range between 0.07 and 0.23, F1 scores as high as 0.92, predictive intervals with coverage greater than 85%, and latency ranging from 25 to 200 ms. The suggested DT framework provides potential for the predictive, adaptive, and patient-specific operation of devices that hold promise for generalizable intelligent medical devices that approach human factors in medical devices of the next generation.