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 may be experiencing heart failure.
While helpful, echocardiograms can be labour-intensive procedures that are only offered at select hospitals.
In the study, the researchers at Mount Sinai described the development of an artificial intelligence (AI)-based computer algorithm that not only assessed the strength of the left ventricle but also the right ventricle, which takes deoxygenated blood streaming in from the body and pumps it to the lungs.
The algorithm was 94 per cent accurate at predicting which patients had a healthy ejection fraction and 87 per cent accurate at predicting those who had an ejection fraction that was below 40 per cent.
The algorithm also learned to detect right valve weaknesses from the electrocardiograms with 84 per cent accuracy at predicting which patients had weak right valves.
“We showed that deep-learning algorithms can recognise blood pumping problems on both sides of the heart from ECG waveform data,” said Benjamin S. Glicksberg, 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 summarising the results of corresponding echocardiograms taken from the same patients.
In this situation, the written reports acted as a standard set of data for the computer to compare with the electrocardiogram data and learn how to spot weaker hearts.
“Our results suggest that this algorithm could be a useful tool for helping clinical practitioners combat heart failure suffered by a variety of patients,” added Glicksberg. “We are in the process of carefully designing prospective trials to test out its effectiveness in a more real-world setting.”