Key result
Reduced-lead AI ECG screening requires 54 clusters to detect a ~5-point sensitivity difference.
Why the study?
Artificial intelligence models trained on standard twelve-lead electrocardiogram recordings may lose diagnostic sensitivity when deployed on portable single-lead and reduced-lead devices used in school-based rheumatic heart disease screening.
Does calibration transfer preserve diagnostic sensitivity of artificial intelligence electrocardiogram models when deployed on portable reduced-lead devices compared to standard twelve-lead recordings in school-based rheumatic heart disease screening?
Does calibration transfer preserve diagnostic sensitivity of artificial intelligence electrocardiogram models when deployed on portable reduced-lead devices compared to standard twelve-lead recordings in school-based rheumatic heart disease screening?
Treating calibration transfer as a design parameter identifies the statistical conditions under which reduced-lead ECG screening programs can preserve AI diagnostic sensitivity for rheumatic heart disease without requiring full 12-lead acquisition.
May caution against unvalidated reduced-lead AI for RHD screening; extends linear operator theory but leaves empirical validation open.
Portable single-lead and reduced-lead electrocardiogram devices are increasingly used in school-based rheumatic heart disease screening, but artificial intelligence models trained on standard twelve-lead recordings may lose diagnostic sensitivity when deployed on devices with different lead configurations. The framework treats lead configuration as a linear observation operator acting on the cardiac electrical source, and derives a sensitivity bound as a function of the condition number of the transfer operator and the calibration error of the deployed model. A before-after-control-impact design is characterised under a declared variance structure with between-cluster standard deviation of 6.0, residual standard deviation of 4.5, and intraclass correlation of 0.64, using 2000 replications per design cell across cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points. The design characterisation indicates that 54 clusters are required to detect a 5 percentage point effect at 80 percent power. Calibration transfer should be treated as a design parameter rather than a post-hoc correction, and identifies the conditions under which reduced-lead screening programmes can preserve diagnostic sensitivity without full twelve-lead acquisition.
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Abdul-Razak Issahaku (2026) studied Rheumatic heart disease. AI electrocardiogram interpretation on portable single-lead and reduced-lead devices vs. Standard twelve-lead electrocardiogram acquisition was evaluated on Diagnostic sensitivity in school-based rheumatic heart disease screening. AI ECG screening on reduced-lead devices requires 54 clusters to detect a 5 percentage point diagnostic sensitivity effect at 80% power (ICC 0.64).
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