AI-based detection and classification of anomalous aortic origin of coronary arteries using coronary CT angiography images.

Journal: Nature communications
PMID: 40169568

Abstract

Anomalous aortic origin of the coronary artery (AAOCA) is a rare cardiac condition that can lead to ischemia or sudden cardiac death, yet it is often overlooked or falsely classified in routine coronary CT angiography (CCTA). Here, we developed, validated, externally tested, and clinically evaluated a fully automated artificial intelligence (AI)-based tool for detecting and classifying AAOCA in 3D-CCTA images. The discriminatory performance of the different models achieved an AUC ≥ 0.99, with sensitivity and specificity ranging 0.95-0.99 across all internal and external testing datasets. Here, we present an AI-based model that enables fully automated and accurate detection and classification of AAOCA, with the potential for seamless integration into clinical workflows. The tool can deliver real-time alerts for potentially high-risk AAOCA anatomies, while also enabling the analysis of large 3D-CCTA cohorts. This will support a deeper understanding of the risks associated with this rare condition and contribute to improving its future management.

Authors

  • Isaac Shiri
    Biomedical and Health Informatics, Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran.
  • Giovanni Baj
    Department of Cardiology, Inselspital Bern University Hospital, University of Bern, Freiburgstrasse, Bern, CH - 3010, Switzerland.
  • Pooya Mohammadi Kazaj
    Department of Cardiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
  • Marius R Bigler
    Department of Cardiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
  • Anselm W Stark
    Department of Cardiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
  • Waldo Valenzuela
    Insel Data Science Center, Inselspital, Bern University Hospital, Murtenstrasse 42, CH-3008, Bern, Switzerland.
  • Ryota Kakizaki
    Department of Cardiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
  • Matthias Siepe
    Center for Congenital Heart Disease, Department of Cardiac Surgery, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
  • Stephan Windecker
    Department of Cardiology, Inselspital, Bern University Hospital, University of Bern, 3010 Bern, Switzerland.
  • Lorenz Räber
    Department of Cardiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
  • Andreas A Giannopoulos
    From the Applied Imaging Science Laboratory, Department of Radiology, Brigham and Women's Hospital, 75 Francis St, Boston, MA 02115 (T.C., A.A.G., K.K.K., F.J.R., D.M.); Harvard T.H. Chan School of Public Health, Boston, Mass (S.Y.); and Department of Radiology, Brigham and Women's Hospital, Boston, Mass (T.K., B.R.).
  • George Cm Siontis
    Department of Cardiology, Bern University Hospital, Inselspital, University of Bern, Freiburgstrasse 18, Bern, CH-3010, Switzerland. georgios.siontis@insel.ch.
  • Ronny R Buechel
    Department of Nuclear Medicine, Cardiac Imaging, University and University Hospital Zurich, Ramistrasse 100, 8091, Zurich, Switzerland. Electronic address: ronny.buechel@usz.ch.
  • Christoph Gräni
    Department of Cardiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.

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