External assessment of an artificial intelligence-enabled electrocardiogram for aortic stenosis detection.

ABSTRACT:

Aims

To assess the performance of an artificial intelligence-enabled electrocardiogram (AI-ECG) algorithm in identifying patients with moderate to severe aortic stenosis (AS) in an Asian cohort from a tertiary care centre.

Methods and results

We identified a randomly selected patients ≥60 years old who underwent echocardiography and ECG within in 31 days between 2012 and 2021 at the Samsung Medical Center in Korea. Patients with previous cardiac surgery, prosthetic valves, or pacemakers were excluded. The AI-ECG model, originally developed and validated by Mayo Clinic in the USA, was applied without fine-tuning. Performance metrics, including the area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accura

SUBMITTER: Kim D 

PROVIDER: S-EPMC12282354 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature