An AI-based multitask framework for cardiac function assessment through echocardiograms

Abstract

The accurate assessment of cardiac function is crucial for preventing and controlling cardiovascular diseases and reducing global mortality rates. In recent years, the rapid advancement of machine learning and deep learning, particularly the utilization of artificial intelligence technologies such as convolutional neural networks and multi-task learning, has significantly improved the objectivity and precision of assessing echocardiogram images. However, existing methods lack a thorough exploration of the intrinsic relationship between ejection fraction (EF), end-diastolic volume (EDV), and end-systolic volume (ESV) calculations, which ultimately influence the accuracy of cardiac function assessment. Therefore, we propose an AI-Based Multi-task Framework for Cardiac Function Assessment through echocardiograms. The framework utilizes a 3DCNN network to concurrently extract spatial and temporal features from echocardiographic videos. It employs multi-task learning by assigning varying weights to sub-tasks, enhancing the prediction accuracy of ejection fraction through joint training. Experimental results on the publicly available Echonet-Dynamic dataset demonstrate that the proposed framework achieves promising performance in ejection fraction prediction, with mean absolute error, root mean square error, and R² scores of 3.89%, 5.13%, and 0.82, respectively, surpassing other comparative methods. This framework will further aid clinicians in more accurate cardiac function assessment, offering promising prospects for its practical application.