A deep learning method for the automated assessment of paradoxical pulsation after myocardial infarction using multicenter cardiac MRI data

Abstract

Objective: The current study aimed to explore a deep convolutional neural network (DCNN) model that integrates multidimensional CMR data to accurately identify LV paradoxical pulsation after reperfusion by primary percutaneous coronary intervention with isolated anterior infarction.

Methods: A total of 401 participants (311 patients and 90 age-matched volunteers) were recruited for this prospective study. The two-dimensional UNet segmentation model of the LV and classification model for identifying paradoxical pulsation were established using the DCNN model. Features of 2- and 3-chamber images were extracted with 2-dimensional (2D) and 3D ResNets with masks generated by a segmentation model. Next, the accuracy of the segmentation model was evaluated using the Dice score and classification model by receiver operating characteristic (ROC) curve and confusion matrix. The areas under the ROC curve (AUCs) of the physicians in training and DCNN models were compared using the DeLong method.

Results: The DCNN model showed that the AUCs for the detection of paradoxical pulsation were 0.97, 0.91, and 0.83 in the training, internal, and external testing cohorts, respectively (p < 0.001). The 2.5-dimensional model established using the end-systolic and end-diastolic images combined with 2-chamber and 3-chamber images was more efficient than the 3D model. The discrimination performance of the DCNN model was better than that of physicians in training (p < 0.05).

Conclusions: Compared to the model trained by 2-chamber or 3-chamber images alone or 3D multiview, our 2.5D multiview model can combine the information of 2-chamber and 3-chamber more efficiently and obtain the highest diagnostic sensitivity.

Clinical relevance statement: A deep convolutional neural network model that integrates 2-chamber and 3-chamber CMR images can identify LV paradoxical pulsation which correlates with LV thrombosis, heart failure, ventricular tachycardia after reperfusion by primary percutaneous coronary intervention with isolated anterior infarction.

Key points: • The epicardial segmentation model was established using the 2D UNet based on end-diastole 2- and 3-chamber cine images. • The DCNN model proposed in this study had better performance for discriminating LV paradoxical pulsation accurately and objectively using CMR cine images after anterior AMI compared to the diagnosis of physicians in training. • The 2.5-dimensional multiview model combined the information of 2- and 3-chamber efficiently and obtained the highest diagnostic sensitivity.

Keywords: Deep learning; Magnetic resonance imaging; Myocardial infarction; Ventricular remodeling.