Evaluation of stenoses using AI video models applied to coronary angiography

The coronary angiogram is the gold standard for evaluating the severity of coronary artery disease stenoses. Presently, the assessment is conducted visually by cardiologists, a method that lacks standardization. This study introduces DeepCoro, a ground-breaking AI-driven pipeline that integrates adv...

Full description

Saved in:
Bibliographic Details
Published in:NPJ digital medicine 2024-05, Vol.7 (1), p.138-138, Article 138
Main Authors: Labrecque Langlais, Élodie, Corbin, Denis, Tastet, Olivier, Hayek, Ahmad, Doolub, Gemina, Mrad, Sebastián, Tardif, Jean-Claude, Tanguay, Jean-François, Marquis-Gravel, Guillaume, Tison, Geoffrey H., Kadoury, Samuel, Le, William, Gallo, Richard, Lesage, Frederic, Avram, Robert
Format: Article
Language:English
Subjects:
631/114/1305
631/114/1564
692/699/75
Biomedicine
Biotechnology
Cardiology
Cardiovascular disease
Coronary vessels
Medical imaging
Medicine
Medicine & Public Health
Vein & artery diseases
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The coronary angiogram is the gold standard for evaluating the severity of coronary artery disease stenoses. Presently, the assessment is conducted visually by cardiologists, a method that lacks standardization. This study introduces DeepCoro, a ground-breaking AI-driven pipeline that integrates advanced vessel tracking and a video-based Swin3D model that was trained and validated on a dataset comprised of 182,418 coronary angiography videos spanning 5 years. DeepCoro achieved a notable precision of 71.89% in identifying coronary artery segments and demonstrated a mean absolute error of 20.15% (95% CI: 19.88–20.40) and a classification AUROC of 0.8294 (95% CI: 0.8215–0.8373) in stenosis percentage prediction compared to traditional cardiologist assessments. When compared to two expert interventional cardiologists, DeepCoro achieved lower variability than the clinical reports (19.09%; 95% CI: 18.55–19.58 vs 21.00%; 95% CI: 20.20–21.76, respectively). In addition, DeepCoro can be fine-tuned to a different modality type. When fine-tuned on quantitative coronary angiography assessments, DeepCoro attained an even lower mean absolute error of 7.75% (95% CI: 7.37–8.07), underscoring the reduced variability inherent to this method. This study establishes DeepCoro as an innovative video-based, adaptable tool in coronary artery disease analysis, significantly enhancing the precision and reliability of stenosis assessment.
ISSN:2398-6352
2398-6352
DOI:10.1038/s41746-024-01134-4