Advanced artificial intelligence (AI) models can detect signs of significant valvular disease and ventricular dysfunction using a single point-of-care ultrasound (POCUS) image, according to new data published in Frontiers in Digital Health.
Researchers see potential for this technology to make screening much easier for physicians who are not trained cardiologists.
For this particular study, researchers retrospectively evaluated more than 120,000 transthoracic echocardiograms annotated by board-certified cardiologists to train and validate their AI model.
A prospective group of more than 200 additional real-world patients then underwent POCUS by non-cardiologist physicians to test the AI's effectiveness.
In retrospective testing, the AI's area under the ROC curves (AUCs) were 0.883 for mitral regurgitation, 0.913 for tricuspid regurgitation, 0.940 for ventricular dysfunction and 0.982 for reduced ejection fraction.
With the real-world patient cohort, meanwhile, AUCs were 0.72, 0.87, 0.95 and 0.97, respectively.
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