Structural Heart Disease Screening Using Artificial Intelligence-Enabled Electrocardiogram and Novice Handheld Cardiac Ultrasound: A Cost Analysis

Abstract Aims Early detection of structural heart disease (SHD) improves patient outcomes. However, population-based screening is not recommended due to the lack of accurate and cost-effective tools. We evaluated the costs of artificial intelligence-enabled electrocardiogram (AI-ECG) alone versus AI-ECG followed by handheld cardiac ultrasound (HCU) for SHD screening. Methods We performed a model-based cost analysis using data from 286 adult patients who underwent ECG and same-day HCU performed by a novice operator. Transthoracic echocardiogram (TTE) was the reference standard. We compared two screening strategies: (1) AI-ECG alone and (2) a stepwise approach (AI-ECG followed by HCU). We assessed costs per diagnosis of aortic stenosis (AS), increased left ventricular wall thickness (ILVWT), and left ventricular systolic dysfunction (LVSD). Sensitivity analyses were conducted for varying disease prevalence. Results The stepwise approach decreased the cost per diagnosis of AS from 6, 386 (AI-ECG alone) to 2, 746 (57. 0% savings), ILVWT from 4, 448 to 2, 895 (34. 9% savings), and LVSD from 1, 469 to 1, 296 (11. 8% savings). Overall, the cost per diagnosis for all SHDs combined decreased from 1, 940 to 1, 570 (19. 1% savings). Sensitivity analysis demonstrated that cost savings were inversely proportional to disease prevalence. Nevertheless, stepwise screening remained cost-saving compared to AI-ECG alone until prevalence exceeded approximately 55. 9% for AS, 28. 9% for ILVWT, 20. 7% for LVSD, and 40. 8% for all SHDs combined. Conclusion A stepwise screening strategy incorporating HCU after a positive AI-ECG reduces the immediate costs of SHD detection by minimizing unnecessary TTEs. This approach may enhance the feasibility of population-based SHD screening, particularly in lower-prevalence settings.