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JMIR Medical Informatics

A Deep Learning Model using Transfer Learning for Detecting Left Ventricular Systolic Dysfunction

  • Apr. 2026
  • by Young Jun Park et. al.

Abstract

Background:Artificial intelligence–augmented electrocardiogram (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 2 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 measurements and prior AI-ECG outputs to adjust for future predictions, thus empirically mitigating confounding effects. Pretraining was also implemented to enhance the model’s performance.

 

Results:The recalibrated 12-lead DeepECG LVSD model achieved an area under the receiver operating curve of 0.956 (95% CI 0.946‐0.965) for internal validation and 0.940 (95% CI 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 area under the receiver operating curve of 0.953 (95% CI 0.941‐0.965) in the internal validation and 0.947 (95% CI 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<.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.

JMIR Med Inform 2026;14:e83127

Link

https://medinform.jmir.org/2026/1/e83127/

Author

Young Jun Park, MD,†*, Sungjae Lee, MS†, Jung-Woo Son, MD, †, Min-Soo Ahn MD, PhDa, Sang-Jin Han, MD, PhDc, Taehyun Joo, MDb, Yeongyeon Na, MSb, Sunghoon Joo, PhDb, Hyunjin Ahn, MDb, Mineok Chang, MD, PhDb, Yeha Lee, PhDb, Sung-Ai Kim, MD, PhDc,*

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