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Life sciences · Preprint
arXiv · September 4, 2026
Early or partial results. Treat as a signal, not a conclusion.
This preprint reports a machine learning approach using β-VAE features and reconstruction errors to discriminate LGE-positive from LGE-negative cardiomyopathy patients on ECG. The best model (ECGx.AI foundation model) achieved AUC 0.686, while reconstruction error-based classification yielded AUC 0.643; these results are modest and lack independent validation, representing early proof-of-concept rather than clinical readiness.
Single-center retrospective machine learning validation study. Local cohort of 300 cardiomyopathic patients classified by presence or absence of Late Gadolinium Enhancement (LGE) on cardiac MRI; specific inclusion/exclusion criteria, demographics, and clinical setting not stated.. Intervention: β-VAE and ECGx.AI ECG feature extraction; DTW reconstruction error calculation.. Compared with: Comparison between LGE+ and LGE- patients; no traditional diagnostic reference standard (e.g., standard ECG criteria) explicitly compared.. n = 300. Not stated; described as 'local cohort'..
ECGx.AI model reached AUC 0.686 with Random Forest classifier β-VAE model achieved AUC 0.577 with Gradient Boosting; sensitivity 0.775 DTW-reconstruction errors differed significantly between LGE+ and LGE- in 10 of 12 ECG leads (Mann-Whitney U test)
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These results are too preliminary to guide clinical practice. The modest discrimination (best AUC 0.686) and absence of external validation mean ECG-based VAE features cannot yet replace or supplement LGE-MRI for scar detection in routine clinical use.
Single-center proof-of-concept study using machine learning on ECG to detect myocardial scar; modest discrimination (AUC 0.686 best) without peer review, establishing feasibility rather than clinical utility.
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Quoted from the source exactly as published.
These results are too preliminary to guide clinical practice. The modest discrimination (best AUC 0.686) and absence of external validation mean ECG-based VAE features cannot yet replace or supplement LGE-MRI for scar detection in routine clinical use.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
Late Gadolinium Enhancement (LGE) on cardiac magnetic resonance is a key marker of myocardial scar, but its limited accessibility motivates routine ECG-based screening. We evaluated whether $β$-variational autoencoder (VAE)-derived ECG representations can discriminate LGE+ from LGE- cardiomyopathic patients in a local cohort of 300 subjects. We compared 32-dimensional features from the foundation ECGx.AI model with those from a shallower $β$-VAE trained on normal PTB-XL ECGs, evaluating downstream classification and Dynamic Time Warping (DTW)-based reconstruction errors. ECGx.AI reached an area under ROC of 0.686 with Random Forest, while the proposed $β$-VAE reached 0.577 with sensitivity of 0.775 with Gradient Boosting. Notably, DTW-reconstruction errors significantly differed between classes in 10 out of 12 leads according to Mann-Whitney U test and help in classification, leading to an area under ROC of 0.643 with Logistic Regression, supporting their potential as markers of scar-related ECG alterations.
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