Machine learning aids diagnosis of athletes' heart conditions

Heart monitor

Computer algorithms can interpret echocardiographic images and distinguish between two similar heart conditions affecting young athletes, according to research published in the Journal of the American College of Cardiology.

Researchers from the Icahn School of Medicine at Mount Sinai tested three different machine-learning algorithms for their effectiveness in the discrimination of physiological versus pathological hypertrophic cardiomyopathy.

Pathological hypertrophic cardiomyopathy (HCM), in which a portion of the myocardium enlarges, leading to impaired heart function, is the leading cause of sudden death in young athletes. Distinguishing between it and physiological hypertrophy (heart enlargement often due to exercise) generally requires testing for the two conditions with interpretation by a highly trained cardiologist, according to an announcement.

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"This demonstrates how machine-learning models and other smart interpretation systems could help to efficiently analyze and process large volumes of cardiac ultrasound data, and with the growth of telemedicine, it could enable cardiac diagnoses even in the most resource-burdened areas," said study author Joel Dudley, Ph.D.

With machines’ the ability to consider millions of variables, better algorithms hold the potential to improve diagnostic accuracy, according to a recent article in The New England Journal of Medicine. For instance, it cites computers’ ability to look for anomalies at the pixel level of radiographs.

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However, researchers from Northwestern University and the Rehabilitation Institute of Chicago called machine learning “voodoo” in a study published at at bioRxiv, calling for proper evaluation of the methods used for compiling data for smartphone apps and wearable sensors.

RELATED: Researchers call machine learning 'voodoo,' warn of inaccuracies

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