A multicenter study developed an AI-enabled electrocardiography (AI-ECG) model that predicts vessel-specific hemodynamically significant stenosis using coronary computed tomographic angiography (CCTA) as the reference. The model demonstrated strong discrimination in internal and external cohorts, including normal ECGs, and produced low-, intermediate-, and high-risk strata that correlated with stenosis severity and major adverse cardiovascular events. Calibration, decision curve analyses, and integration with guideline-based pre-test probability showed clinical utility, while waveform and attribution analyses revealed physiologically meaningful ECG features linked to high-risk predictions.
arXiv:2606. 02605v1 Announce Type: cross Abstract: Coronary artery stenosis is a common cardiovascular disease, with severe, untreated cases posing significant risks of heart attack.
arXiv:2607. 16323v1 Announce Type: cross Abstract: Electrocardiography (ECG) is an inexpensive, standard-of-care test for cardiac symptoms, but front-line triage often lacks immediate access to definitive imaging such as echocardiography (ECHO) or cardiac magnetic resonance (CMR).
arXiv:2608.27690v1 Announce Type: cross Abstract: Cardiovascular risk prediction remains limited by incomplete clinical data and imaging biomarkers that reduce computed tomography (CT) to a small num...
The study demonstrates that contrastive pre‑training of ECG representations using cardiac magnetic resonance (CMR) imaging data can enhance ECG‑based detection of Chagas disease. By aligning an ECG encoder with a CMR embedding space from 63,193 paired UK Biobank examinations, the authors achieved higher AUROC and sensitivity metrics on CODE‑15%, SaMi‑Trop, and PhysioNet/CinC 2025 Challenge datasets compared to an unaligned baseline. The approach shows that imaging‑supervised ECG representations generalize across different populations and resource‑constrained settings.
arXiv:2609.21755v1 Announce Type: new Abstract: Emergency department (ED) decision-making relies on heterogeneous clinical information, including patient history, vital signs, laboratory results, and...
arXiv:2606. 10725v1 Announce Type: new Abstract: Background.
arXiv:2607. 07683v1 Announce Type: new Abstract: Electrocardiography (ECG) is one of the most widely used tests for diagnosing cardiovascular disease.
The study investigates whether ECG representations derived from a β-variational autoencoder (VAE) can distinguish patients with myocardial scar (LGE+) from those without (LGE-) using routine ECG data. In a cohort of 300 cardiomyopathic patients, the β-VAE achieved an AUC of 0.577 (sensitivity 0.775) with Gradient Boosting, while the foundation ECGx.AI model reached an AUC of 0.686 with Random Forest. Dynamic Time Warping reconstruction errors differed significantly between classes in most leads and improved classification to an AUC of 0.643 with Logistic Regression, suggesting these errors could serve as markers of scar-related ECG changes.
arXiv:2606. 28179v1 Announce Type: cross Abstract: Identifying robust associations between cardiac imaging phenotypes and clinical diseases is fundamental to population-scale cardiovascular research and reliable risk stratification.
arXiv:2607. 24035v1 Announce Type: cross Abstract: Explainable AI (XAI) is used to assess whether artificial intelligence models rely on meaningful patterns, yet explanations that appear plausible for individual predictions may systematically misrepresent model behavior.
arXiv:2608. 14723v1 Announce Type: cross Abstract: Reduced left ventricular ejection fraction (LVEF) is frequently asymptomatic and often detected only after advanced heart failure develops.
arXiv:2609.37750v1 Announce Type: cross Abstract: Pulmonary embolism (PE) is a leading cause of cardiovascular mortality, yet the real-world performance of FDA-cleared AI detection models remains inc...