Artificial Intelligence Electrocardiogram Interpretation for Rheumatic Heart Disease Screening Under Variable Portable Device Lead Placement: A Calibration-Transfer Evaluation of Healthcare Referral Yield in School-Age Children

Portable single-lead electrocardiogram (ECG) devices promise to extend rheumatic heart disease (RHD) screening into schools where echocardiography is unavailable, but their lead placement varies with operator and child anatomy, and artificial intelligence (AI) models trained on standard 12-lead recordings may not transfer. The framework separates lead-placement-induced distribution shift into geometric, impedance and operator components, and derives a referral-yield bound that depends on calibration slope and intercept under shift. Applied to published estimates, the framework predicts that uncalibrated transfer can inflate referral volume by a factor of 1.5 to 2.3 while reducing positive predictive value below 20% in low-prevalence school populations. The DHS analysis shows that household computer ownership, a proxy for digital infrastructure, remains low (mean 7.893%) and highly variable (range 1.50–22.00%), constraining where AI-ECG screening can be deployed with adequate connectivity. The contribution is a transferable evaluation protocol that makes calibration, not discrimination, the primary criterion for referral safety in RHD screening.