Key result
Targeted recalibration of portable AI-ECG models outperforms full retraining for echocardiography referral triage.
Why the study?
Portable single-lead and reduced-lead ECG devices are proposed for rheumatic heart disease triage where echocardiography is scarce, but their AI interpretation models are typically trained on controlled twelve-lead recordings. The impact of this lead-configuration mismatch on the calibration and operating characteristics of transferred triage models across echocardiography referral pathways needed formal evaluation.
Does recalibration on a small target-configuration sample improve the post-transfer calibration curve and referral-pathway operating value compared to retraining for AI electrocardiogram triage of rheumatic heart disease under lead-configuration differences?
Does recalibration on a small target-configuration sample improve the post-transfer calibration curve and referral-pathway operating value compared to retraining for AI electrocardiogram triage of rheumatic heart disease under lead-configuration differences?
This study provides a formal calibration-centered framework showing that when transferring 12-lead AI ECG models to portable reduced-lead devices for rheumatic heart disease triage, recalibration on a small target sample recovers more referral-pathway operating value than retraining.
Calibration strategies for lead mismatch in AI-ECG RHD models remain untested in field conditions; leaves open whether they meaningfully improve triage accuracy.
Portable single-lead and reduced-lead electrocardiogram devices are increasingly proposed as triage instruments for rheumatic heart disease in settings where echocardiography is scarce, yet the artificial intelligence models that interpret them are almost always trained on twelve-lead recordings acquired in controlled conditions. the present analysis treats the resulting mismatch as a calibration problem rather than a simple accuracy problem, and asks what happens to the operating characteristics of a transferred triage model when the lead configuration at deployment differs from the lead configuration at training. A formal framework is developed in which lead-configuration shift is represented as a structured perturbation of the input distribution, and in which the clinically relevant quantity is the post-transfer calibration curve that determines how many true cases are referred for echocardiography at any fixed referral budget. The framework yields three propositions: that discrimination and calibration degrade at different rates under lead shift; that the degradation is asymmetric across disease prevalence bands; and that recalibration on a small target-configuration sample recovers more of the referral pathway's operating value than retraining on the same sample. A design-based evaluation is specified to test these propositions, with the before-after-control-impact estimator characterised under a declared variance structure (between-cluster SD = 6.0, residual SD = 4.5, intraclass correlation = 0.64) across 2000 replications per design cell. The contribution is a calibration-centred account of transfer failure that reframes portable electrocardiogram triage as a problem of referral-pathway operating characteristics rather than of raw classification accuracy.
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Okonkwo et al. (2026) studied Rheumatic heart disease. Recalibration of transferred AI electrocardiogram triage model on a target lead-configuration sample vs. Retraining on the same target-configuration sample was evaluated on Post-transfer calibration curve and echocardiography referral pathway operating value at a fixed referral budget. Recalibrating portable AI-ECG triage models on a small target-configuration sample recovers more referral-pathway value than retraining across 2000 replications (ICC=0.64).
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