Echo Doppler Flow Classification and Goodness Assessment with Convolutional Neural Networks

Abstract

Doppler Echocardiography is critical for measuring abnormal cardiac function and diagnosing valvular stenosis and regurgitation. The current practice for assessing and interpreting Doppler echo images is time-consuming and depends highly on the experience of the operator. The limitations of this practice can be mitigated using fully automated intelligent systems. Essential first steps toward comprehensive computer-assisted Doppler echocardiographic interpretation include automatic classification into view/flow categories and goodness assessment of these flows. In this paper, we propose a deep learning-based method for Doppler flow classification and goodness assessment. The method has been trained on labeled images representing a wide range of real-world clinical variation. Our method, when evaluated on unseen data, achieved overall accuracies of 91.6% and 88.9% for flow classification and goodness assessment, respectively. While further research is needed, these results are encouraging and prove the feasibility of using fully automated intelligent systems for analyzing and interpreting Doppler echo images.