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Updated: Sep 25, 2025
Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System
Published on: April 11, 2025
Artificial Intelligence-Enabled Electrocardiogram Predicted Left Ventricle Diameter as an Independent Risk Factor of
Hung-Yi Chen1, Chin-Sheng Lin2, Wen-Hui Fang3,1,4
1Department of Internal Medicine, National Defense Medical Center, Tri-Service General Hospital, Taipei, Taiwan.
Insights
An AI-enabled ECG system can detect increased left ventricular end-diastolic diameter (LV-D), a key heart failure risk factor. This technology identifies high-risk patients and predicts adverse cardiovascular outcomes, improving early detection of heart failure.
Area of Science:
- Cardiology
- Artificial Intelligence
- Medical Imaging
Background:
- Heart failure (HF) presents a significant global health challenge with poor survival rates.
- Early identification of high-risk HF patients is crucial but difficult.
- Increased left ventricular end-diastolic diameter (LV-D) is a known risk factor for HF and adverse cardiovascular (CV) outcomes.
Purpose of the Study:
- To develop an artificial intelligence (AI)-enabled electrocardiogram (ECG) system for early detection of LV-D increase.
- To validate a deep learning model (DLM) for predicting LV-D and LV-systolic diameter (LV-S).
- To investigate the relationship between ECG-derived LV-D (ECG-LV-D) and echocardiographic LV-D, and its predictive value for future CV outcomes.
Main Methods:
- A DLM was trained using 99,692 ECGs and paired echocardiography data.
- Internal and external validation were performed using 7,551 and 11,644 ECGs from different hospitals.
- The association between ECG-LV-D and CV outcomes (LV dysfunction, CV mortality, AMI, CAD) was analyzed.
Main Results:
- The DLM achieved high accuracy in detecting mild and severe LV-D dilation (AUCs 0.83-0.93).
- High ECG-LV-D in patients with normal ejection fraction (EF) correlated with increased risk of LV dysfunction, CV mortality, AMI, and CAD (HRs 1.59-2.34).
- ECG-LV-D independently predicted new-onset LV dysfunction in the external validation set (HR 1.88).
Conclusions:
- The AI-enabled ECG system effectively detects increased LV-D.
- ECG-LV-D identifies high-risk patients, including those with normal EF.
- ECG-LV-D serves as an independent predictor of long-term adverse CV outcomes.
Background:
Heart failure (HF) is a global disease with increasing prevalence in an aging society. However, the survival rate is poor despite the patient receiving standard treatment. Early identification of patients with a high risk of HF is important but challenging. Left ventricular end-diastolic diameter (LV-D) increase was an independent risk factor of HF and adverse cardiovascular (CV) outcomes. In this study, we aimed to develop an artificial intelligence (AI) enabled electrocardiogram (ECG) system to detect LV-D increase early.
Objective:
We developed a deep learning model (DLM) to predict left ventricular end-diastolic and end-systolic diameter (LV-D and LV-S) with internal and external validations and investigated the relationship between ECG-LV-D and echocardiographic LV-D and explored the contributions of ECG-LV-D on future CV outcomes.
Methods:
Electrocardiograms and corresponding echocardiography data within 7 days were collected and paired for DLM training with 99,692 ECGs in the development set and 20,197 ECGs in the tuning set. The other 7,551 and 11,644 ECGs were collected from two different hospitals to validate the DLM performance in internal and external validation sets. We analyzed the association and prediction ability of ECG-LVD for CV outcomes, including left ventricular (LV) dysfunction, CV mortality, acute myocardial infarction (AMI), and coronary artery disease (CAD).
Results:
The mean absolute errors (MAE) of ECG-LV-D were 5.25/5.29, and the area under the receiver operating characteristic (ROC) curves (AUCs) were 0.8297/0.8072 and 0.9295/0.9148 for the detection of mild (56 ≦ LV-D < 65 mm) and severe (LV-D ≧ 65 mm) LV-D dilation in internal/external validation sets, respectively. Patients with normal ejection fraction (EF) who were identified as high ECHO-LV-D had the higher hazard ratios (HRs) of developing new onset LV dysfunction [HR: 2.34, 95% conference interval (CI): 1.78-3.08], CV mortality (HR 2.30, 95% CI 1.05-5.05), new-onset AMI (HR 2.12, 95% CI 1.36-3.29), and CAD (HR 1.59, 95% CI 1.26-2.00) in the internal validation set. In addition, the ECG-LV-D presents a 1.88-fold risk (95% CI 1.47-2.39) on new-onset LV dysfunction in the external validation set.
Conclusion:
The ECG-LV-D not only identifies high-risk patients with normal EF but also serves as an independent risk factor of long-term CV outcomes.
