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

Isaac Shiri, Giovanni Baj, Pooya Mohammadi Kazaj, Marius R. Bigler, Anselm W. Stark, Waldo Valenzuela, Ryota Kakizaki, Matthias Siepe, Stephan Windecker, Lorenz Räber, Andreas A. Giannopoulos, George CM. Siontis, Ronny R. Buechel and Christoph Gräni ()
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Isaac Shiri: University of Bern
Giovanni Baj: University of Bern
Pooya Mohammadi Kazaj: University of Bern
Marius R. Bigler: University of Bern
Anselm W. Stark: University of Bern
Waldo Valenzuela: Freiburgstrasse
Ryota Kakizaki: University of Bern
Matthias Siepe: University of Bern
Stephan Windecker: University of Bern
Lorenz Räber: University of Bern
Andreas A. Giannopoulos: University Hospital Zurich
George CM. Siontis: University of Bern
Ronny R. Buechel: University Hospital Zurich
Christoph Gräni: University of Bern

Nature Communications, 2025, vol. 16, issue 1, 1-11

Abstract: 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.

Date: 2025
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DOI: 10.1038/s41467-025-58362-9

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