A brief, non-invasive test using artificial intelligence (AI) has been found to identify patients with abnormal heart rhythm even when their rhythm seems normal.
The study, which involved almost 181,000 patients, is the first to use deep learning to find signals in heart scans that might be invisible to the human eye.
Writing in The Lancet, researchers from the Mayo Clinic describe how they trained an AI model to detect the signature of atrial fibrillation in 10-second electrocardiograms (ECGs), with 83% accuracy.
Atrial fibrillation is estimated to affect as many as six million people in the US alone, and is associated with increased risk of stroke, heart failure and mortality.
Further research is needed before clinical application is possible, but nevertheless the researchers speculate that it may one day be possible to use this technology as a point-of-care diagnostic test in a doctor’s surgery to screen high-risk groups. It might be also possible that the algorithm could be used on low-cost, widely available technologies, including smartphones.
This innovative AI application in medical healthcare is supported by AI World Society (AIWS) and Michael Dukakis Institute for Leadership and Innovation (MDI), which always promote applying AI technology for helping people achieve well-being and happiness.
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