The value of AI-based analysis of fractional flow reserve of volume computed tomographically detected coronary artery stenosis with regard to their hemodynamic relevance.

Journal: RoFo : Fortschritte auf dem Gebiete der Rontgenstrahlen und der Nuklearmedizin

PMID: 38631371

Abstract

PURPOSE: The aim of our work was to demonstrate the importance of artificial intelligence-based analysis of fractional flow reserves of computed tomographically detected coronary artery stenosis with regard to their hemodynamic relevance in patients with unclear chest pain and suspected stable coronary heart disease with a low to medium pre-test probability.

Authors

Hans-Jürgen Noblé

Department of Radiology, German Air Force Center of Aerospace Medicine, Cologne, Germany.
Nadine Mühlbauer

Department of Radiology, German Air Force Center of Aerospace Medicine, Cologne, Germany.
Josef Ehling

Department of Radiology, German Air Force Center of Aerospace Medicine, Cologne, Germany.
Paul Martin Bansmann

Institute for Diagnostic and Interventional Radiology, Hospital Porz am Rhein, Cologne, Germany.

Keywords

Aged Artificial Intelligence Computed Tomography Angiography Coronary Angiography Coronary Stenosis Female Fractional Flow Reserve, Myocardial Hemodynamics Humans Male Middle Aged Retrospective Studies Sensitivity and Specificity Tomography, X-Ray Computed

External Resources

View on PubMed Access via DOI PubMed (38631371)

The value of AI-based analysis of fractional flow reserve of volume computed tomographically detected coronary artery stenosis with regard to their hemodynamic relevance.

Abstract

Authors

Keywords

External Resources

Popular Topics

Recent Journals