An AI model then analysed this data to find patterns, other than AFib itself that distinguished people with AFib from those without.This new model holds the potential to better detect those who are at risk for AFib and ultimately prevent this heart condition’s severe side effects, including stroke and heart failure.
“With this new tool, we can better identify patients at high risk of AFib for further tests and interventions,” said Giorgio Quer, Director of AI at Scripps Research Translational Institute and an assistant professor of digital medicine at Scripps Research.”Long term, this can help drive the right resources to the right people and potentially reduce the incidence of stroke and heart failure,” said Giorgio Quer.The irregular heartbeat due to AFib can cause blood to pool in the heart and form blood clots, which can then contribute to strokes.
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AFib is also associated with an increased risk of heart failure or death.Importantly, the model remained accurate for both an older population, who are at higher risk of AFib, and people under the age of 55, who are at much lower risk and are usually excluded from general AFib screening.
“Patients with frequent AFib episodes can be identified easily with an ECG recorded over at least a week,” said Giorgio Quer,” said Giorgio Quer.
Source: IANS
