A novel physics-guided deep learning network improves the explainability and physical consistency of non-invasive coronary hemodynamic assessments.
The rapid development of the Internet of Things (IoT) widely supports the smart healthcare system. IoT-based smart health has significant importance for the diagnosis of cardiovascular disease (CVD) in clinical practice. Combined with advanced artificial intelligence techniques, IoT-based smart health provides valuable and accurate diagnosis information remotely for cardiovascular disease. The functional assessment of CVD is an essential task in clinical practice. It aims to determine the extent of myocardial ischemia through the measurement of the hemodynamic parameters of the coronary artery. However, the clinical adoption of the hemodynamic parameters is limited due to the potential risks and high health costs during measurements. Recent advances in artificial intelligence have enabled the computation of hemodynamic parameters based on the anatomical features of coronary arteries. However, the existing methods still lack explainability in the prediction. To address this issue, we present a physics-guided deep learning network for the functional assessment of CVD in an IoT-based manner. We specifically design an attentive network to determine the effective features by considering the importance of coronary artery anatomy features and artery segments. To obtain the functional assessment with explainability, we incorporate physical knowledge related to the blood flow into the loss function. It can ensure that functional assessment follows the physical laws. Extensive experiments are performed on a synthetic data set and a real-world clinical data set. The results show that our approach can achieve accurate and physically consistent assessment. Moreover, our method promotes deeper adoption of IoT and deep learning in the field of smart health.
Zhang et al. (Mon,) studied this question.