Diagnostic performance of deep learning to exclude coronary stenosis on CT angiography in TAVI patients.

We evaluated the diagnostic performance of a deep-learning model (DLM) (CorEx®, Spimed-AI, Paris, France) designed to automatically detect > 50% coronary stenosis on coronary computed tomography angiography (CCTA) images. We studied inter-observer variability as an additional aim. CCTA images obtained before transcatheter aortic valve implantation (TAVI) were assessed by two radiologists and the DLM, and the results were compared to those of invasive coronary angiography (ICA) used as the reference standard.

[Benefits and drawbacks of CT scan as a triple rule-out exam in acute chest pain to exclude acute coronary syndrome, pulmonary embolism and aortic dissection].

Chest pain is one of the major causes for admission in the Emergency Room in most countries and one of the principal reasons for urgent consultation with a cardiologist or a general practitioner. After clinical examination and initial biological measurements, substantial patients require further explorations. CT scan allows the search for pulmonary embolism in the early stage of pulmonary arteries iodine contrast exploration.

MDCT planning of trans catheter aortic valve implantation (TAVI): determination of optimal c-arm angulation.

A fluoroscopic view perpendicular to the aortic valve annulus is required during transcatheter aortic valve implantation (TAVI) for obtaining an optimal deployment of the bioprosthesis. By predicting c-arm angulation, pre-procedural MDCT could decrease the number of aortograms, shorten the time of the procedure and reduce the amount of intra-arterial contrast agent. The aim of our study was to assess the accuracy of MDCT in predicting c-arm angulation at the cath.