Automated estimation of computed tomography-derived left ventricular mass using sex-specific 12-lead ECG-based temporal convolutional network

To propose a novel deep learning-based method, the eLVMass-Net, for the estimation of left ventricular mass (LVM) based on 12-lead electrocardiograms (ECGs).

We developed a deep learning model for LVM estimation using raw ECG signals, demographic data, and ECG parameters as input by using TW-CVAI dataset (n = 1459). Synchronized single-heartbeat waveforms were processed using a temporal convolutional network (TCN). Ground-truth LVM values were obtained from coronary computed tomography angiography. We performed external validation on an independent NTUH dataset (n = 2579). To account for sex-specific differences in left ventricular remodelling and body habitus, we further developed separate models for males and females. We compared the performance of the eLVMass-Net, with two state-of-the-art (SOTA) models.

Non-sex-specific eLVMass-Net achieved a mean absolute error (MAE) of 14.3 ± 0.7 g and a mean absolute percentage error (MAPE) of 12.9 ± 1.1% between predicted and ground-truth LVM values under five-fold cross-validation. The eLVMass-Net outperformed two SOTA models in terms of both LVM estimation and left ventricular hypertrophy (LVH) classification. Sex-specific design was superior in LVH classification based on estimated LVM (c-statistic: 0.77 ± 0.05 for male model; 0.75 ± 0.05 for female model; 0.70 ± 0.02 for non-sex-specific model; P  < 0.01 between both sex-specific models vs. non-sex-specific model). The saliency maps revealed gender-specific differences in how the model weighted ST-T segment features for LVM prediction.

The proposed eLVMass-Net outperformed previously published approaches by ECG pre-processing with synchronized single heartbeat extraction and TCN as ECG encoder. Additionally, the development of sex-specific models proved to be a rational approach.