CARDINAL Predicts Cardiovascular Risk From Non-contrast Cardiac CT
Cardiovascular risk prediction remains limited by incomplete clinical data and imaging biomarkers that reduce computed tomography (CT) to a small number of handcrafted features. We developed CARDINAL (Cardiovascular Assessment via Representation learning from Deep Imaging with Nested Anatomical Latent embeddings), a clinically grounded framework that learns compact representations from routine non-contrast cardiac CT for major adverse cardiovascular event (MACE) prediction. In 17,659 patients, CARDINAL was evaluated for 1-, 3-, 5-, and 10-year MACE prediction against American Heart Association (AHA) pooled cohort equations (PCE), AHA predicting risk of cardiovascular disease events (PREVE...
| Authors | Roy Gabriel, Nattakorn Kittisut, Jamshid Hassanpour et al. |
|---|---|
| Primary category | eess.IV |
| Published | 27 August 2026 |
| Last updated at source | 27 August 2026 |
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