The transformer-based ECG model achieved a higher AUROC of 0.924 compared to ResNet-18's AUROC of 0.912, indicating superior performance in detecting LVSD.
Does a transformer-based ECG foundation model improve the detection of left ventricular systolic dysfunction compared to a standard ResNet-18 deep learning model?
A transformer-based ECG foundation model outperforms a conventional ResNet-18 deep learning model in detecting left ventricular systolic dysfunction, offering a highly sensitive tool for rapid rule-out in clinical settings.
Absolute Event Rate: 0% vs 0%
Abstract Background Early identification or rule-out of left ventricular systolic dysfunction (LVSD) is critical for optimizing clinical care pathways. AI-enabled analysis of 12-lead resting ECGs offers a low-cost and scalable solution, but direct comparisons between foundation models and conventional deep learning architectures remain limited. Purpose To evaluate the performance of a novel time-series foundation model (TSFM) for detecting LVSD from ECGs and compare it with a standard ResNet-18 deep learning (DL) model. Methods Both models were trained and tested using 31,832 Heart Center Leipzig ECG–echocardiogram pairs, split into training (n = 20,372), validation (n = 5,093), and test (n = 6,367) cohorts. TSFM was additionally pre-trained on 33K unlabeled ECGs using masked pre-training. LVSD was defined as LVEF ≤ 40%, with a prevalence of 15% across all sets. Model performance was evaluated using AUROC, AUPRC, and standard classification metrics. Results On the test dataset, TSFM achieved a higher AUROC (0.924 95% CI: 0.918–0.928) than ResNet-18 (0.912 95% CI: 0.904–0.916, p 0.001), and a slightly higher but not statistically significant AUPRC (0.695 0.663–0.723 vs. 0.646 0.612–0.683). TSFM also demonstrated improved sensitivity (0.89 vs. 0.87) and negative predictive value (0.98 vs. 0.97), with identical specificity (0.81) and positive predictive value (0.44). Conclusions The transformer-based ECG model outperforms a conventional DL model in detecting LVSD. Its high negative predictive value supports further evaluation in triage, emergency, and preoperative settings where rapid rule-out of LVSD can inform downstream clinical decision-making.
Bollmann et al. (Thu,) reported a other. The transformer-based ECG model achieved a higher AUROC of 0.924 compared to ResNet-18's AUROC of 0.912, indicating superior performance in detecting LVSD.