Prognostic value of a novel artificial intelligence-based coronary CTA-derived ischemia algorithm among patients with normal or abnormal myocardial perfusion.

Journal: Journal of cardiovascular computed tomography
PMID: 38664074

Abstract

BACKGROUND: Among patients with obstructive coronary artery disease (CAD) on coronary computed tomography angiography (CTA), downstream positron emission tomography (PET) perfusion imaging can be performed to assess the presence of myocardial ischemia. A novel artificial-intelligence-guided quantitative computed tomography ischemia algorithm (AI-QCT) aims to predict ischemia directly from coronary CTA images. We aimed to study the prognostic value of AI-QCT among patients with obstructive CAD on coronary CTA and normal or abnormal downstream PET perfusion.

Authors

  • Sarah Bär
    Turku PET Centre, Turku University Hospital and University of Turku, Turku, Finland; Department of Cardiology, Bern University Hospital Inselspital, Bern, Switzerland.
  • Teemu Maaniitty
    Turku PET Centre, Turku University Hospital and University of Turku, Turku, Finland; Department of Clinical Physiology, Nuclear Medicine, and PET, Turku University Hospital, Turku, Finland.
  • Takeru Nabeta
    Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands.
  • Jeroen J Bax
    Departments of Cardiology, Heart Lung Centre, Leiden University Medical Center, Leiden, The Netherlands.
  • James P Earls
    Cleerly inc., New York, United States.
  • James K Min
    3 Department of Radiology, Weill Cornell Medicine , New York, New York.
  • Antti Saraste
    Heart Center, Turku University Hospital, University of Turku, Turku, Finland.
  • Juhani Knuuti
    Turku PET Centre, University of Turku and Turku University Hospital, Kiinamyllynkatu 4-8, 20520, Turku, Finland.

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