Can an inverse analysis methodology using recurrent neural networks accurately calculate systemic blood pressure waveforms and detect abdominal aortic aneurysms from non-invasive peripheral pressure inputs?
A novel recurrent neural network-based inverse analysis method can estimate central cardiovascular hemodynamics and detect abdominal aortic aneurysms using non-invasive peripheral pressure waveforms, advancing the development of cardiovascular digital twins.
An exponential rise in patient data provides an excellent opportunity to improve the existing health care infrastructure. In the present work, a method to enable cardiovascular digital twin is proposed using inverse analysis. Conventionally, accurate analytical solutions for inverse analysis in linear problems have been proposed and used. However, these methods fail or are not efficient for nonlinear systems, such as blood flow in the cardiovascular system (systemic circulation) that involves high degree of nonlinearity. To address this, a methodology for inverse analysis using recurrent neural network for the cardiovascular system is proposed in this work, using a virtual patient database. Blood pressure waveforms in various vessels of the body are inversely calculated with the help of long short-term memory (LSTM) cells by inputting pressure waveforms from three non-invasively accessible blood vessels (carotid, femoral and brachial arteries). The inverse analysis system built this way is applied to the detection of abdominal aortic aneurysm (AAA) and its severity using neural networks.
Chakshu et al. (Fri,) studied this question.