Structural heart disease screening using artificial intelligence–enabled electrocardiogram and novice handheld cardiac ultrasound: a cost analysis

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 vs. AI-ECG followed by handheld cardiac ultrasound (HCU) for SHD screening. Methods and results: 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: (i) AI-ECG alone and (ii) 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. The stepwise approach decreased the cost per diagnosis of AS from $6386 (AI-ECG alone) to $2746 (57.0% savings), ILVWT from $4448 to $2895 (34.9% savings), and LVSD from $1469 to $1296 (11.8% savings). Overall, the cost per diagnosis for all SHDs combined decreased from $1940 to $1570 (19.1% savings). Sensitivity analysis demonstrated that cost savings were inversely proportional to disease prevalence. Nevertheless, stepwise screening remained cost-saving compared with AI-ECG alone until prevalence exceeded ∼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.