Automated stenosis estimation of coronary angiographies using end-to-end learning.

ABSTRACT: The initial evaluation of stenosis during coronary angiography is typically performed by visual assessment. Visual assessment has limited accuracy compared to fractional flow reserve and quantitative coronary angiography, which are more time-consuming and costly. Applying deep learning might yield a faster and more accurate stenosis assessment. We developed a deep learning model to classify cine loops into left or right coronary artery (LCA/RCA) or "other". Data were obtained by manual annotation. Using these classifications, cine loops before revascularization were identified and curated automatically. Separate deep learning models for LCA and RCA were developed to estimate stenosis using these identified cine loops. From a cohort of 19,414 patients and 332,582 cine loops, we identified c

SUBMITTER: Eschen CK 

PROVIDER: S-EPMC11880145 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature