Key result
Deep neural network outperforms standard methods for arterial blood flow reconstruction, achieving ~0.97 correlation.
Why the study?
Existing clinical diagnostic techniques for arterial stenosis are not suitable for early cardiovascular disease prediction and monitoring due to high cost and complex operation.
A novel deep neural network method demonstrates high accuracy in reconstructing arterial blood flow profiles in simulated carotid artery models, potentially aiding early diagnosis of arterial stenosis.
Supports AI flow reconstruction; hypothesis-generating in simulated models and requires in vivo validation before clinical use.
Arterial stenosis will reduce the blood flow to various organs or tissues, causing cardiovascular diseases. Although there are mature diagnostic techniques in clinical practice, they are not suitable for early cardiovascular disease prediction and monitoring due to their high cost and complex operation. In this paper, we studied the electromagnetic effect of arterial blood flow and proposed a method based on the deep neural network for arterial blood flow profile reconstruction. The potential difference and weight matrix are used as inputs to the method, and its output is an estimate of the internal blood flow velocity distribution for arterial blood flow profile reconstruction. Firstly, the weight matrix is input into the convolutional auto-encode (CAE) network to extract its features. Then, the weight matrix features and potential difference are combined to obtain the features of the blood velocity distribution. Finally, the velocity features are reconstructed into blood flow velocity distribution by a convolution neural network (CNN). All data sets are obtained from a model of the carotid artery with different rates of stenosis in a uniform magnetic field by COMSOL. The results show that the average root mean square error of the reconstruction results obtained by the proposed method is 0.0333, and the average correlation coefficient is 0.9721, which is better than the corresponding indicators of the Tikhonov, back propagation (BP) and CNN methods. The simulation results show that the proposed method can achieve high accuracy in blood flow profile reconstruction and is of great significance for the early diagnosis of arterial stenosis and other vessel diseases.
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Yang et al. (2021) studied Arterial stenosis. Deep neural network method (CAE and CNN) vs. Tikhonov, back propagation (BP), and CNN methods was evaluated on Blood flow velocity distribution reconstruction accuracy (root mean square error and correlation coefficient). A deep neural network method for arterial blood flow profile reconstruction outperformed standard methods, achieving an average root mean square error of 0.0333 and correlation coefficient of 0.9721.
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