Publications by authors named "Jonathan Walterspiel"
Purpose: This study evaluates the diagnostic performance of artificial intelligence (AI)-based coronary computed tomography angiography (CCTA) for detecting coronary artery disease (CAD) and assessing fractional flow reserve (FFR) in asymptomatic male marathon runners.
Material And Methods: We prospectively recruited 100 asymptomatic male marathon runners over the age of 45 for CAD screening. CCTA was analyzed using AI models (CorEx and Spimed-AI) on a local server.
Article Synopsis
- The study aimed to assess how effective automated deep learning is in detecting coronary artery disease (CAD) using photon-counting coronary CT angiography (PC-CCTA) images.
- A total of 140 patients were analyzed, showing that the AI models had high sensitivity and specificity, with deep learning achieving a notable accuracy in diagnosing significant CAD.
- The conclusion suggests that AI can significantly assist human experts in clinical practice by enhancing the evaluation of CAD through traditional imaging methods.
Purpose: The purpose of this study was to evaluate the capabilities of photon-counting (PC) CT combined with artificial intelligence-derived coronary computed tomography angiography (PC-CCTA) stenosis quantification and fractional flow reserve prediction (FFRai) for the assessment of coronary artery disease (CAD) in transcatheter aortic valve replacement (TAVR) work-up.
Materials And Methods: Consecutive patients with severe symptomatic aortic valve stenosis referred for pre-TAVR work-up between October 2021 and June 2023 were included in this retrospective tertiary single-center study. All patients underwent both PC-CCTA and ICA within three months for reference standard diagnosis.