US researchers have developed an electrocardiogram-reading algorithm that can detect subtle signs of heart failure. Heart failure, or congestive heart failure, occurs when the heart pumps less blood than the body normally needs. For years, doctors have relied heavily on an imaging technique called an echocardiogram to assess whether a patient is experiencing heart failure. While helpful, echocardiograms can be labor-intensive procedures that are only offered in select hospitals. In the study, Mount Sinai researchers describe the development of an artificial intelligence (AI)-based computer algorithm that measured the strength of not only the left ventricle, but also the right ventricle, which pumps deoxygenated blood through the body. does. It’s for the lungs.
The algorithm was 94 percent accurate in predicting which patients had a healthy ejection fraction and 87 percent accurate predictors who had an ejection fraction that was less than 40 percent.
The algorithm learned to detect right valve weaknesses from electrocardiograms with 84 percent accuracy, allowing it to predict which patients had weak right valves.
“We have shown that deep-learning algorithms can detect problems pumping blood to both sides of the heart from ECG waveform data,” said Benjamin S. Glixberg, assistant professor of genetics and genomic science at Mount Sinai.
For the study, published in the ‘Journal of the American College of Cardiology: Cardiovascular Imaging’, the team programmed a computer to read patient electrocardiograms, along with data extracted from written reports, with echocardiograms taken from the same patients. Summarized the results.
In this situation, the written report served as a standard set of data for the computer to compare with the electrocardiogram data and how to recognize weak hearts.
“Our results suggest that this algorithm may be a useful tool to help clinical practitioners deal with heart failure by a variety of patients,” said Glixberg. “We are in the process of carefully designing prospective trials to test its effectiveness in a more real-world setting.”
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