Back to the list
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
Methods and materials The local Institutional Review Board approved the protocol of this prospective, single-center, observational study. All subjects gave written, informed consent. Consecutive patients referred for standard of care imaging on an EID-CT system were recruited for a research CT scan within 30 days on a PCD-CT. Research scans were conducted on a first-generation clinical dual-source PCD-CT system using the UHR acquisition mode with a gantry rotation time of 0.25 seconds and a collimation of 120×0.2 mm.

An AI algorithm automatically assessed percentage diameter stenosis (PDS) and CAD-RADS category for patients undergoing CCTA on both EID-CT and PCD-CT within 30 days.

Two board-certified radiologists, both with 6 years of experience in cardiovascular imaging, independently evaluated the images using commercial software for the manual coronary assessment (MCA) using the 18-segment coronary model recommended by the Society of Cardiovascular Computed Tomography

 The results were validated in a subset of patients who underwent subsequent invasive coronary angiography (ICA). After a washout period of 3 months, reader 2 repeated all assessments this time using the ACA as assistance (aACA). The time taken for each reading by all experts and the ACA was recorded.
GALLERY