Advancing cardiovascular medicine with machine learning: Progress, potential, and perspective

Recent advances in machine learning (ML) have made it possible to analyze high-dimensional and complex data-such as free text, images, waveforms, videos, and sound-in an automated manner by successfully learning complex associations within these data. Cardiovascular medicine is particularly well poised to take advantage of these ML advances, due to the widespread digitization of medical data and the large num-ber of diagnostic tests used to evaluate cardiovascular disease. Various ML approaches have successfully been applied to cardiovascular tests and diseases to automate interpretation, accurately perform measure-ments, and, in some cases, predict novel diagnoses from less invasive tests, effectively expanding the utility of more widely accessible diagnostic tests. Here, we present examples of some impactful advances in car-diovascular medicine using ML across a variety of modalities, with a focus on deep learning applications.

Automated summary

Recent advances in machine learning have enabled the automated analysis of complex and high-dimensional data in cardiovascular medicine, leading to improved diagnosis, measurement, and prediction capabilities. These advances have significant implications in expanding the utility of diagnostic tests and improving the overall understanding of cardiovascular diseases.