AI-augmented ECG for pre-echocardiography triage: a tool to optimize cardiac imaging utilization.

Aims: Echocardiography is a key diagnostic modality for cardiac dysfunction but is often over utilized due to variability in pre-test clinical assessment. There is a need for a scalable, cost-effective screening tool that can reduce unnecessary referrals without compromising diagnostic accuracy. To develop and validate an AI tool that uses standard 12-lead ECG images to predict the presence of major echocardiographic abnormalities, including reduced ejection fraction (EF ≤35%), valvular heart disease, and elevated pulmonary artery pressure, as a triage tool prior to echocardiography. Methods and results: 51,055 patients aged ≥15 years from a tertiary cardiac care centre, which underwent ECG and echocardiography on the same day between January 2021, and February 2024 were identified. ECGs were stored as images and pre-processed for model input. Echocardiographic findings were extracted using structured reports and regular expression-based keyword searches. The final dataset ( Conclusion: The proposed AI model accurately identifies major echocardiographic abnormalities from ECG images, achieving high NPV and demonstrating strong generalizability.