Researchers believe this new technology can make a very big difference. (Representative Pic)
Researchers in Israel have built an artificial intelligence (AI) tool that analyses ECG tests and predicts heart failure weeks before it actually happens.
According to the Times of Israel, the new technology is currently being used for patients who suffer from myositis or muscle inflammation, a condition which elevates the risk of heart failure. The AI model was updated by feeding the data from ECG scans and medical records of 89 patients suffering from myositis between 2000 and 2020. The algorithm then built a picture of subtle patterns in ECGs which was linked to an increased chance of heart failure.
Speaking to the outlet, Dr Shahar Shelly of Rambam Healthcare Campus who led the research said that this was the first AI tool which has been built especially for this population. He explained that the tool analyses heart patterns that are unique to them and can bring about earlier detection than is currently possible. In his research, Dr Shelly said that the algorithm successfully predicted 80 percent of heart failure cases among a sample of myositis patients.
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"We are running ECG tests through the AI model, which sees details that doctors can't normally detect and then predicts who is at risk of heart failure," said Dr Shelly.
"Given that it's these cardiac dysfunctions that often end up killing people, this can save lives," he added.
The study was conducted by Dr Shelly and other researchers of the Cardiology Department at the US-based Mayo Clinic Medical Center. Even though the new technology hasn't been deployed in clinics, Dr Shelly believes that down the road, the use of this model will allow the provision of appropriate treatments at an early stage, even before the deterioration of the patients' medical condition.
"We are talking here about preventing serious illness and even deaths," Dr Shelly told the outlet. "For a population that is at heightened risk of heart failure, this can make a very big difference," he added.