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Related Experiment Video

Updated: Jan 24, 2026

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations
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Automated estimation of computed tomography-derived left ventricular mass using sex-specific 12-lead ECG-based

Heng-Yu Pan1,2, Benny Wei-Yun Hsu3, Chun-Ti Chou3

  • 1Division of Cardiology, Department of Internal Medicine, National Taiwan University Hospital Hsin-Chu Branch, Hsin-Chu City, Taiwan.

European Heart Journal. Digital Health
|January 23, 2026
PubMed
Summary

A novel deep learning method, eLVMass-Net, accurately estimates left ventricular mass (LVM) from ECGs. Sex-specific models improve left ventricular hypertrophy (LVH) classification, outperforming existing approaches.

Keywords:
Cardiac imagingDeep learningElectrocardiogramLeft ventricular massSex-specific modelsTemporal convolutional network

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Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Left ventricular mass (LVM) is a crucial indicator of cardiovascular health.
  • Accurate LVM estimation is vital for diagnosing and managing cardiac conditions.
  • Current non-invasive methods for LVM assessment have limitations.

Purpose of the Study:

  • To introduce eLVMass-Net, a deep learning model for estimating LVM using 12-lead ECGs.
  • To evaluate the performance of eLVMass-Net against state-of-the-art methods.
  • To investigate the utility of sex-specific models for improved LVM estimation and left ventricular hypertrophy (LVH) classification.

Main Methods:

  • Developed eLVMass-Net using raw ECG signals, demographic data, and ECG parameters from the TW-CVAI dataset (n=1459).
  • Processed synchronized single-heartbeat waveforms with a temporal convolutional network (TCN).
  • Validated externally on the NTUH dataset (n=2579) and compared with two SOTA models, including sex-specific variations.

Main Results:

  • Non-sex-specific eLVMass-Net achieved MAE of 14.3 ± 0.7g and MAPE of 12.9 ± 1.1% via five-fold cross-validation.
  • eLVMass-Net outperformed SOTA models in both LVM estimation and LVH classification.
  • Sex-specific models demonstrated superior LVH classification (c-statistic: 0.77 male, 0.75 female) compared to the non-sex-specific model (0.70).

Conclusions:

  • eLVMass-Net, with synchronized single heartbeat extraction and TCN, surpasses previous ECG-based LVM estimation methods.
  • The development of sex-specific models is a rational and effective approach for enhancing diagnostic accuracy.
  • The model's saliency maps indicated gender-specific feature weighting in the ST-T segment for LVM prediction.