Ensembled Prediction of Rheumatic Heart Disease from Ungated Doppler Echocardiography Acquired in Low-Resource Settings

Rheumatic heart disease (RHD) is a common medical condition in children in which acute rheumatic fever causes permanent damage to the heart valves, thus impairing the heart's ability to pump blood. Doppler echocardiography is a popular diagnostic tool used in the detection of RHD. However, the execution of this assessment requires the work of skilled physicians, which poses a problem of accessibility, especially in low-income countries with limited access to clinical experts. This paper presents a novel, automated, deep learning-based method to detect RHD using color Doppler echocardiography clips. We first homogenize the analysis of ungated echocardiograms by identifying two acquisition views (parasternal and apical), followed by extracting the left atrium regions during ventricular systole. Then, we apply a model ensemble of multi-view 3D convolutional neural networks and a multi-view Transformer to detect RHD. This model allows our analysis to benefit from the inclusion of spatiotemporal information and uses an attention mechanism to identify the relevant temporal frames for RHD detection, thus improving the ability to accurately detect RHD. The performance of this method was assessed using 2,136 color Doppler echocardiography clips acquired at the point of care of 591 children in low-resource settings, showing an average accuracy of 0.78, sensitivity of 0.81, and specificity of 0.74. These results are similar to RHD detection conducted by expert clinicians and superior to the state-of-the-art approach. Our novel model thus has the potential to improve RHD detection in patients with limited access to clinical experts.