Fully automated deep learning powered calcium scoring in patients undergoing myocardial perfusion imaging

Thomas Sartoretti, Antonio G. Gennari, Elisabeth Sartoretti, Stephan Skawran, Alexander Maurer, Ronny R. Buechel, and Michael Messerli
J Nucl Cardiol.
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This study aimed to assess the accuracy of a fully automated deep learning (DL) based coronary artery calcium scoring (CACS) tool using non-contrast CT scans acquired for attenuation correction (AC) of cardiac SPECT-MPI. A total of 56 patients with suspected coronary artery disease were prospectively enrolled. CACS was manually assessed and compared to the cloud-based DL tool's results. The interscore agreement between the standard of reference and the DL tool was 0.986, with a 0.977 agreement in risk categories and a 3.6% reclassification rate. Factors such as heart rate, image noise, BMI, and scan did not significantly impact the absolute percentage difference in CAC scores. The study concluded that the DL tool (AVIEW CAC, Coreline Soft) enables accurate and fully automated estimation of CAC scores in patients undergoing non-contrast CT for AC of SPECT-MPI.


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