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Title: | Invasive fractional-flow-reserve prediction by coronary CT angiography using artificial intelligence vs. computational fluid dynamics software in intermediate-grade stenosis | Authors: | PETERS, Benjamin Paul, Jean-Francois Symons, Rolf FRANSSEN, Wouter Nchimi, Alain GHEKIERE, Olivier |
Issue Date: | 2024 | Publisher: | SPRINGER | Source: | The international journal of cardiovascular imaging, | Status: | Early view | Abstract: | Coronary computed angiography (CCTA) with non-invasive fractional flow reserve (FFR) calculates lesion-specific ischemia when compared with invasive FFR and can be considered for patients with stable chest pain and intermediate-grade stenoses according to recent guidelines. The objective of this study was to compare a new CCTA-based artificial-intelligence deep-learning model for FFR prediction (FFRAI) to computational fluid dynamics CT-derived FFR (FFRCT) in patients with intermediate-grade coronary stenoses with FFR as reference standard. The FFRAI model was trained with curved multiplanar-reconstruction CCTA images of 500 stenotic vessels in 413 patients, using FFR measurements as the ground truth. We included 37 patients with 39 intermediate-grade stenoses on CCTA and invasive coronary angiography, and with FFRCT and FFR measurements in this retrospective proof of concept study. FFRAI was compared with FFRCT regarding the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy for predicting FFR <= 0.80. Sensitivity, specificity, PPV, NPV, and diagnostic accuracy of FFRAI in predicting FFR <= 0.80 were 91% (10/11), 82% (23/28), 67% (10/15), 96% (23/24), and 85% (33/39), respectively. Corresponding values for FFRCT were 82% (9/11), 75% (21/28), 56% (9/16), 91% (21/23), and 77% (30/39), respectively. Diagnostic accuracy did not differ significantly between FFRAI and FFRCT (p = 0.12). FFRAI performed similarly to FFRCT for predicting intermediate-grade coronary stenoses with FFR <= 0.80. These findings suggest FFRAI as a potential non-invasive imaging tool for guiding therapeutic management in these stenoses. | Notes: | Peters, B (corresponding author), Hasselt Univ, Fac Med & Life Sci, LCRC, B-3590 Diepenbeek, Belgium.; Peters, B (corresponding author), Jessa Hosp, Dept Radiol, LCRC, Stadsomvaart 11, B-3500 Hasselt, Belgium. Benjamin.peters@jessazh.be |
Keywords: | Fractional flow reserve;Coronary computed tomography angiography;Artificial intelligence;Deep learning;Intermediate coronary stenoses | Document URI: | http://hdl.handle.net/1942/43431 | ISSN: | 1569-5794 | e-ISSN: | 1875-8312 | DOI: | 10.1007/s10554-024-03173-0 | ISI #: | WOS:001263057700003 | Rights: | The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons. org/licenses/by/4.0/. | Category: | A1 | Type: | Journal Contribution |
Appears in Collections: | Research publications |
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s10554-024-03173-0.pdf | Early view | 816.78 kB | Adobe PDF | View/Open |
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