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Congress: ECR25
Poster Number: C-10229
Type: Poster: EPOS Radiologist (scientific)
Authorblock: G. Tremamunno1, A. Varga-Szemes2, D. Kravchenko2, M. T. Hagar2, U. J. Schoepf2, A. Laghi1, M. Vecsey-Nagy2, T. S. Emrich2; 1Rome/IT, 2Charleston, SC/US
Disclosures:
Giuseppe Tremamunno: Nothing to disclose
Akos Varga-Szemes: Research/Grant Support: Siemens Healtineers
Dmitrij Kravchenko: Nothing to disclose
Muhammad Taha Hagar: Nothing to disclose
Uwe Joseph Schoepf: Research/Grant Support: Siemens Healthineers
Andrea Laghi: Nothing to disclose
Milán Vecsey-Nagy: Nothing to disclose
Tilman Stephan Emrich: Research/Grant Support: Siemens Healthineers
Keywords: Artificial Intelligence, CAD, CT-Angiography, CAD, Arteriosclerosis
Purpose

Artificial intelligence (AI) is rapidly advancing in cardiac imaging, with numerous software tools either under development or already available for automated, fast, and accurate evaluation of the coronary vessels.1The recent introduction of dual-source photon-counting detector CT (PCD-CT) in clinical practice has exhibited several technical advantages over conventional energy-integrating detector (EID)-CT, such as higher contrast-to-noise ratios, reduced electronic noise, and improved spatial resolution.2-3

To date, automatic coronary analysis (ACA) software solutions have been employed and tested exclusively on EID-CTs, leaving their utility for UHR PCD-CT unknown.

Therefore, the aim of this study was to validate the ACA of an artificial intelligence-based prototype on UHR PCD-CCTA data through an intra-individual comparison with conventional EID-CCTA, and ICA serving as a reference, while also evaluating the time efficiency between automated, manual, and combined evaluation methods.

 
GALLERY