The purpose of this study was to evaluate the diagnostic performance of automated deep learning in the detection of coronary artery disease (CAD) on photon-counting coronary CT angiography (PC-CCTA).

This study aim to evaluate the diagnostic performance of AI-based CCTA for detecting
CAD and assessing FFR in asymptomatic male marathon runners.

This study aimed to evaluate the performance of CorEx in classifying coronary artery stenosis in patients eligible for Transcatheter Aortic Valve Replacement (TAVR), using photon-counting CT.

This study aimed to evaluate the performance of FFRAI model to predict positive invasive FFR and compare it to invasive FFR.

This proof-of-concept study aimed to compare the performance of FFR prediction
between an Artificial Intelligence solution and a Computational Fluid Dynamics
(CFD)-based software.

This study aimed to evaluate the performance of CorEx in automatically classifying coronary artery stenosis in emergency department patients with acute chest pain, with suspicion of aortic dissection.

This study aim to evaluate the diagnostic performance of a DLM for quantifying coronary stenosis on computed tomography coronary angiography (CTCA) using the Coronary Artery Disease-Reporting and Data System (CAD-RADS).

This study aimed to evaluate the performance of CorEx in classifying coronary artery stenosis in patients eligible for Transcatheter Aortic Valve Implementation (TAVI), making it possible to avoid unnecessary invasive coronary angiography.

This study aimed to determine the performance of CorEx in classifying patients into CADRADS categories.