Integrating AI-ECG and point-of-care cardiac ultrasound for screening structural heart disease: A proof-of-concept study.

BACKGROUND: Early structural heart disease (SHD) detection is crucial for improving prognostic outcomes, but widely accessible screening methods are lacking. The advent of artificial intelligence-enabled electrocardiograms (AI-ECG) and point-of-care cardiac-ultrasonography (POCCUS) offers promising new approaches for patient screening. We explored the feasibility and potential of integrating these innovative technologies into a practical SHD screening framework. METHODS: Outpatients undergoing ECG at the Mayo Clinic ECG laboratory between November 2023 and February 2024 were pragmatically offered POCCUS, performed by novice operators and reviewed by expert echocardiographers. AI-ECG and POCCUS findings were integrated to assess SHD, including reduced left ventricular systolic function (ejection-fraction<50%), aortic stenosis, and increased left ventricular wall thickness suggestive of cardiac amyloidosis or hypertrophic cardiomyopathy. Operators were blinded to patients' comorbidities and formal echocardiogram results. RESULTS: Of 486 patients (median-age 64 years; 49% women), 286 had available formal echocardiography, with 17.5% having SHD. AI-ECG had a 32% positive predictive value (PPV) and a 94% negative predictive value (NPV) to detect any SHD. Adding POCCUS increased the overall PPV to 64% with an NPV of 93%, with an increase in diagnostic accuracy from 67% to 88%. Notably, 89.5% (17/19) of the "false positives" by AI-ECG + POCCUS had less-than-moderate-SHD. Applying the AI-ECG + POCCUS screening workflow on the entire cohort resulted in a number-needed-to-screen of 8 to identify 1 patient requiring formal echocardiography. CONCLUSIONS: The integration of AI-ECG and POCCUS holds promise as a potentially effective screening method for SHD, facilitating improved patient selection for formal echocardiography.