Evaluation of stenoses using AI video models applied to coronary angiography (2024)

Evaluation of stenoses using AI video models applied to coronary angiography (1) https://doi.org/10.1038/s41746-024-01134-4 · Evaluation of stenoses using AI video models applied to coronary angiography (2)

Journal: npj Digital Medicine, 2024, №1

Publisher: Springer Science and Business Media LLC

Authors:

  1. Élodie Labrecque Langlais
  2. Denis Corbin
  3. Olivier Tastet
  4. Ahmad Hayek
  5. Gemina Doolub
  6. Sebastián Mrad
  7. Jean-Claude Tardif
  8. Jean-François Tanguay
  9. Guillaume Marquis-Gravel
  10. Geoffrey H. Tison
  11. Samuel Kadoury
  12. William Le
  13. Richard Gallo
  14. Frederic Lesage
  15. Robert Avram

Funder Montreal Heart Institute Research Centre, the Montreal Heart Institute Foundation, the Des Groseillers-Bérard Interventional Cardiology Research Chair

Abstract

AbstractThe 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.

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Evaluation of stenoses using AI video models applied to coronary angiography (2024)

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