Wall Street Journal
by John McCormick
Artificial intelligence could help doctors learn more than the condition of a patient’s heart from an electrocardiogram: Applying AI to the heart test data could indicate overall health status, researchers at the Mayo Clinic have discovered. Electrocardiograms, which record electrical activity in the heart to check for heart disease, reflect the effects of normal aging, and the Mayo researchers theorized that the test could estimate a person’s physiological age—a measure of how well someone’s body is working relative to their chronological age. The Mayo researchers used electrocardiogram, or EKG, data they had gathered on more than 500,000 patients from 1994 to 2017 to train a convolutional neural network—a deep learning network that can analyze images—to learn what normal EKGs look like as people age.
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Context: A new Mayo Clinic research study shows that artificial intelligence (AI) can detect the signs of an irregular heart rhythm — atrial fibrillation (AF) — in an EKG, even if the heart is in normal rhythm at the time of a test. In other words, the AI-enabled EKG can detect recent atrial fibrillation that occurred without symptoms or that is impending, potentially improving treatment options. This research could improve the efficiency of the EKG, a noninvasive and widely available method of heart disease screening. The findings and an accompanying commentary are published in The Lancet.
Accuracy and timeliness are important in making an atrial fibrillation diagnosis. Left undetected, atrial fibrillation can cause stroke, heart failure and other cardiovascular disease. Knowing that a patient has atrial fibrillation helps direct treatment with blood thinners, notes Paul Friedman, M.D., chair of the Department of Cardiovascular Medicine at Mayo Clinic. Dr. Friedman, who is a cardiac electrophysiologist, is the study's senior author.
You can read more about the study on Mayo Clinic News Network.