JMIR Publications

ABSTRACT

Background:

Artificial intelligence-augmented electrocardiography (AI-ECG) models for detecting left ventricular systolic dysfunction (LVSD) often exhibit degraded performance in patients with comorbidities.

Objective:

This study aimed to introduce and validate a recalibration method using longitudinal patient data to enhance prediction accuracy and simulate its clinical utility for ongoing monitoring.

Methods:

We conducted a multicenter, retrospective cohort study using data from two hospitals in Korea. A dataset of paired transthoracic echocardiograms (TTEs) and electrocardiograms (ECGs) matched within a 2-week interval, was constructed, separating pairs into baseline (first for each patient) and follow-up assessments. In addition to conventional supervised learning, we developed a patient-wise recalibration strategy that incorporated historical left ventricular ejection fraction (LVEF) measurements and prior AI-ECG outputs to adjust for future predictions, thus empirically mitigating confounding effects. Pretraining was also implemented to enhance the model performance.

Results:

The recalibrated 12-lead DeepECG LVSD model achieved an area under the receiver operating curve (AUROC) of 0.956 (95% confidence interval: 0.946–0.965) for internal validation and 0.940 (0.936–0.945) for external validation of follow-up TTE–ECG pairs. The uncalibrated 12-lead DeepECG LVSD model also showed modest performance, with an AUROC of 0.953 (0.941–0.965) in the internal validation and 0.947 (0.943–0.951) in the external validation when tested on baseline TTE–ECG pairs. Recalibration yielded statistically significant improvements in the 12-lead DeepECG LVSD models (p < 0.001), with enhanced and more balanced performance across all clinical subgroups.

Conclusions:

Patient-wise recalibration improved accuracy and consistency across various comorbidities by mitigating performance degradation and bias. This broadens the application of AI-ECG for LVSD detection from low-risk screening to high-risk longitudinal monitoring.