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...
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.
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:2609.00909v1 Announce Type: new Abstract: Reliable evaluation of automated coronary computed tomography angiography (CCTA) report generation requires standardized multicentre benchmarks and cli...
arXiv:2607. 27404v1 Announce Type: new Abstract: Existing benchmarks for electrocardiogram foundation models primarily evaluate downstream predictive performance, providing limited insight into whether their internal representations can be faithfully decomposed, clinically interpreted, or reproduced across independent analyses.
arXiv:2608. 03597v1 Announce Type: new Abstract: Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia and is associated with increased risks of stroke, heart failure, and mortality.
arXiv:2607. 05009v1 Announce Type: cross Abstract: Complete digital 12-lead electrocardiograms (ECGs) are essential for AI-enabled cardiovascular assessment, yet many clinical ECG records, particularly those digitized from ECG images, remain incomplete because of short display formats, incomplete waveform digitization, lead loss, or signal corruption.