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Mayo, Eko team on machine learning to detect heart abnormalities

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The Mayo Clinic and health technology vendor Eko are working together to develop and commercialize a machine learning-based algorithm that screens patients for low ejection fraction, which is linked to heart failure. A low ejection fraction number, often measured by an echocardiogram, suggests problems with the heart's pumping function. However, echocardiography is an expensive and time-consuming medical imaging test using ultrasound that is less accessible than a doctor with a stethoscope. "With this collaboration, we hope to transform the stethoscope in the pocket of every physician and nurse from a hand tool to a power tool," said Paul Friedman, MD, chair of cardiovascular medicine at the Mayo Clinic. "The community practitioner performing high school sports physicals and the surgeon about to operate may be able to seamlessly tap the knowledge of an experienced cardiologist to determine if a weak heart pump is present simply by putting a stethoscope on a person's chest for a few seconds."


AI Algorithm Aids Early Detection of Low Ejection Fraction

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FRIDAY, May 28, 2021 (HealthDay News) -- An artificial intelligence (AI) algorithm that uses data from electrocardiography can help increase the diagnosis of low ejection fraction (EF), according to a study published online May 6 in Nature Medicine. Xiaoxi Yao, Ph.D., from the Mayo Clinic in Rochester, Minnesota, and colleagues randomly assigned 120 primary care teams, including 358 clinicians, to intervention (access to AI results from the low ejection fraction algorithm developed by Mayo and licensed to Anumana Inc.; 181 clinicians) or control (usual care; 177 clinicians) in a pragmatic trial at 45 clinics and hospitals. A total of 22,641 adult patients with echocardiography performed as part of routine care were included (11,573 in the intervention group; 11,068 controls). The researchers found positive AI results, indicating a high likelihood of low EF, in 6.0 percent of patients in both arms. More echocardiograms were obtained for patients with positive results by clinicians in the intervention group (49.6 versus 38.1 percent), but echocardiogram use was similar in the overall cohort (19.2 versus 18.2 percent).


AI Algorithm Aids Early Detection Of Low Ejection Fraction - AI Summary

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Xiaoxi Yao, Ph.D., from the Mayo Clinic in Rochester, Minnesota, and colleagues randomly assigned 120 primary care teams, including 358 clinicians, to intervention (access to AI results from the low ejection fraction algorithm developed by Mayo and licensed to Anumana Inc.; 181 clinicians) or control (usual care; 177 clinicians) in a pragmatic trial at 45 clinics and hospitals. More echocardiograms were obtained for patients with positive results by clinicians in the intervention group (49.6 versus 38.1 percent), but echocardiogram use was similar in the overall cohort (19.2 versus 18.2 percent). The diagnosis of low EF was increased with the intervention in the overall cohort (2.1 versus 1.6 percent; odds ratio, 1.32) and among patients with positive results (19.5 versus 14.5 percent; odds ratio, 1.43). "The AI intervention increased the diagnosis of low ejection fraction overall by 32 percent relative to usual care.


AI-Enhanced Cardiology Takes Another Step Forward

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John Halamka, M.D., president, Mayo Clinic Platform, and Paul Cerrato, senior research analyst and communications specialist, Mayo Clinic Platform, wrote this article. Asymptomatic left ventricular systolic dysfunction (ALVSD) may not be the most familiar disorder in medicine, but it nonetheless increases a patient's risk of heart failure and death. Unfortunately, ALVSD is not that easily detected. Characterized by low ejection fraction (EF) -- a measure of how much blood the heart pumps out during each contraction -- it's readily diagnosed with an echocardiogram. But because the procedure is expensive, it's not recommended as routine screening for the general public.


AI-Enabled ECG Helps Identify Heart Failure

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The article, "AI-Enabled ECG Improves Ability to Identify Heart Failure in Emergency Departments," was originally published on Practical Cardiology. An artificial intelligence (AI)-enabled electrocardiogram (ECG) could aid clinicians in emergency departments more accurately identify heart failure. Findings from the study indicate the AI-enhanced ECG could improve identification of left ventricular systolic dysfunction in patients presenting the emergency departments with acute dyspnea. "AI-enhanced ECGs are quicker and outperform current standard-of-care tests. Our results suggest that high-risk cardiac patients can be identified quicker in the emergency department and provides an opportunity to link them early to appropriate cardiovascular care," said lead investigator Demilade Adedinsewo, MD, MPH, chief fellow in the division of cardiovascular medicine at Mayo Clinic in Jacksonville, Florida, in a statement.