Using Artificial Intelligence for Rheumatic Heart Disease Detection by Echocardiography: Focus on Mitral Regurgitation

BACKGROUND: Identification of children with latent rheumatic heart disease (RHD) by echocardiography, before onset of symp-

toms, provides an opportunity to initiate secondary prophylaxis and prevent disease progression. There have been limited

artificial intelligence studies published assessing the potential of machine learning to detect and analyze mitral regurgitation or

to detect the presence of RHD on standard portable echocardiograms.

METHODS AND RESULTS: We used 511 echocardiograms in children, focusing on color Doppler images of the mitral valve.

Echocardiograms were independently reviewed by an expert adjudication panel. Among 511 cases, 229 were normal, and

282 had RHD. Our automated method included harmonization of echocardiograms to localize the left atrium during systole

using convolutional neural networks and RHD detection using mitral regurgitation jet analysis and deep learning models with

an attention mechanism. We identified the correct view with an average accuracy of 0.99 and the correct systolic frame with

an average accuracy of 0.94 (apical) and 0.93 (parasternal long axis). It localized the left atrium with an average Dice coefficient

of 0.88 (apical) and 0.9 (parasternal long axis). Maximum mitral regurgitation jet measurements were similar to expert manual

measurements (P value=0.83) and a 9-feature mitral regurgitation analysis showed an area under the receiver operating char-

acteristics curve of 0.93, precision of 0.83, recall of 0.92, and F1 score of 0.87. Our deep learning model showed an area under

the receiver operating characteristics curve of 0.84, precision of 0.78, recall of 0.98, and F1 score of 0.87.

CONCLUSIONS: Artificial intelligence has the potential to detect RHD as accurately as expert cardiologists and to improve with

more data. These innovative approaches hold promise to scale echocardiography screening for RHD.

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