AI algorithms support early detection of low ejection fraction

According to a study published online on May 6, artificial intelligence (AI) algorithms that use data from electrocardiograms help improve the diagnosis of low ejection fraction (EF). Nature medicine..

Dr. Xiaoxi Yao and colleagues at the Mayo Clinic in Rochester, Minnesota randomly assigned 120 primary care teams, including 358 clinicians. intervention Access to AI results with positive results indicating likely low EF in practical trials in 45 clinics and hospitals; 181 clinicians) or controls (conventional care; 177) Human clinician). 22,641 in total Adult patient Echocardiography was performed as part of routine care (11,573 in the intervention group and 11,068 in the control group).

Researchers found that AI results were positive in 6.0% of patients in both groups. Patients who gave positive results by clinicians in the intervention group had more echocardiograms (49.6% vs. 38.1%), but the use of echocardiography was similar across the cohort (19.2). % Vs. 18.2%).The· Diagnosis Interventions in the entire cohort (2.1 to 1.6%; odds ratio 1.32) and positive-resulting patients (19.5 to 14.5%; odds ratio 1.43) increased the proportion of low EF.

“AI intervention increased the overall low ejection fraction diagnosis by 32% compared to normal care. In patients with positive AI results, the relative increase in diagnosis was 43%,” Yao said. He said in a statement. “Absolutely, for every 1,000 patients screened, AI screening provided five new diagnoses with lower ejection fractions than normal care.”

Mayo Clinic has licensed the AI-ECG algorithm to EKO, a manufacturer of digital stethoscopes with embedded ECG electrodes.

Trials show early AI-guided detection of heart disease in daily work

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AI algorithms support early detection of low ejection fraction

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