Élodie Labrecque Langlais, Denis Corbin, Olivier Tastet, Ahmad Hayek, Gemina Doolub, Sebastián Mrad, Jean‐Claude Tardif, Jean‐François Tanguay, Guillaume Marquis‐Gravel, Geoffrey H. Tison, Samuel Kadoury, William Le, Richard Gallo, Frédéric Lesage
and Robert Avram
Article (2024)
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Abstract
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.
Uncontrolled Keywords
cardiovascular diseases; image processing; machine learning
Subjects: |
1900 Biomedical engineering > 1900 Biomedical engineering 1900 Biomedical engineering > 1901 Biomedical technology 2500 Electrical and electronic engineering > 2500 Electrical and electronic engineering |
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Department: |
Department of Electrical Engineering Department of Computer Engineering and Software Engineering |
Funders: | CRSNG/NSERC, Montreal Heart Institute Research Centre, Montreal Heart Institute Foundation, Des Groseillers-Bérard Interventional Cardiology Research Chair, Canadian Institute for Advanced Research (CIFAR), Fonds de la recherche en santé du Québec, Institute for Data Valorization (IVADO), Fonds de recherche du Québec - Nature et technologies (FRQNT) |
Grant number: | 312758 |
PolyPublie URL: | https://publications.polymtl.ca/58571/ |
Journal Title: | NPJ Digital Medicine (vol. 7) |
Publisher: | Springer Nature |
DOI: | 10.1038/s41746-024-01134-4 |
Official URL: | https://doi.org/10.1038/s41746-024-01134-4 |
Date Deposited: | 26 Jun 2024 14:48 |
Last Modified: | 13 Feb 2025 21:22 |
Cite in APA 7: | Labrecque Langlais, É., Corbin, D., Tastet, O., Hayek, A., Doolub, G., Mrad, S., Tardif, J.‐C., Tanguay, J.‐F., Marquis‐Gravel, G., Tison, G. H., Kadoury, S., Le, W., Gallo, R., Lesage, F., & Avram, R. (2024). Evaluation of stenoses using AI video models applied to coronary angiography. NPJ Digital Medicine, 7, 138 (13 pages). https://doi.org/10.1038/s41746-024-01134-4 |
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