Diagnostic Value of Fully Automated Artificial Intelligence Powered Coronary Artery Calcium Scoring from 18F-FDG PET/CT
Claudia Morf, Thomas Sartoretti, Antonio G. Gennari, Alexander Maurer, Stephan Skawran, Andreas A. Giannopoulos, Elisabeth Sartoretti, Moritz Schwyzer, Alessandra Curioni-Fontecedro, Catherine Gebhard, Ronny R. Buechel, Philipp A. Kaufmann, Martin W. Huellner and Michael Messerli
In this study, researchers aimed to assess the feasibility and accuracy of a fully automated artificial intelligence (AI) powered coronary artery calcium scoring (CACS) method on ungated CT scans in oncologic patients undergoing 18F-FDG PET/CT. A total of 100 oncologic patients were retrospectively analyzed, comparing manual CACS on non-contrast ECG-gated CT scans with AI-CACS performed using AVIEW CAC (Coreline Soft) on ungated CT scans from 18F-FDG-PET/CT examinations. The AI-CACS tool demonstrated a sensitivity and specificity of 85% and 90% for detecting CAC, with an interscore agreement of 0.88 between manual and AI methods. Despite a generally underestimated CAC load on ungated CT by AI-CACS, the study concluded that fully automated AI-CACS using AVIEW CAC is feasible and provides an acceptable to good estimation of CAC burden in non-contrast free-breathing, ungated CT scans from 18F-FDG-PET/CT examinations.