CorEx
AI solution for comprehensive CCTA analysis
CorEx is a comprehensive CCTA analysis tool, a deep-learning point-of-care solution that automatically identifies and classifies coronary lesions based on CCTA scans. CorEx also calculates lesion related FFR value at the 0.8 threshold, enabling optimal patient management, with a solution fully integrated in the workflow.
CAD-RADS Classification
CorEx delivers a comprehensive report which highlights the CAD-RADS classification of the CCTA exam, by automatically detecting coronary arteries stenosis and delivering precise quantification.
Functional assessment
CorEx predicts fractional flow reserve (FFR) at the 0.8 threshold, by vessel, using invasive FFR as the gold standard for accuracy.
CorEx Explainability
CorEx highlights regions of interest to enhance insight and explainability for clinical users, reinforcing confidence in reported values and supporting CCTA education
CorEx streamlines case prioritization by automatically generating a CAD-RADS score based on precise stenosis quantification from CCTA exams, enabling critical cases to be triaged and read first. Additionally, CorEx provides a reliable second-opinion feature, enhancing diagnostic confidence for less experienced radiologists.
CorEx predicts fractional flow reserve (FFR) at the 0.8 threshold, having the potential to reduce unnecessary invasive coronary angiographies (ICA) and optimize patient outcomes and treatment pathways.
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Publications
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Peters B, Paul JF, Symons R, Franssen WMA, Nchimi A, Ghekiere O. Invasive fractional-flow-reserve prediction by coronary CT angiography using artificial intelligence vs. computational fluid dynamics software in intermediate-grade stenosis. Int J Cardiovasc Imaging. 2024 Sep;40(9):1875-1880. doi: 10.1007/s10554-024-03173-0. Epub 2024 Jul 4. PMID: 38963591; PMCID: PMC11473557.
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Paul JF, Rohnean A, Giroussens H, Pressat-Laffouilhere T, Wong T. Evaluation of a deep learning model on coronary CT angiography for automatic stenosis detection. Diagn Interv Imaging. 2022 Jun;103(6):316-323. doi: 10.1016/j.diii.2022.01.004. Epub 2022 Jan 26. PMID: 35090845.
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Glessgen CG, Boulougouri M, Vallée JP, Noble S, Platon A, Poletti PA, Paul JF, Deux JF. Artificial intelligence-based opportunistic detection of coronary artery stenosis on aortic computed tomography angiography in emergency department patients with acute chest pain. Eur Heart J Open. 2023 Sep 7;3(5):oead088. doi: 10.1093/ehjopen/oead088. PMID: 37744954; PMCID: PMC10516619.
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Brendel JM, Walterspiel J, Hagen F, Kübler J, Paul JF, Nikolaou K, Gawaz M, Greulich S, Krumm P, Winkelmann M. Coronary artery disease evaluation during transcatheter aortic valve replacement work-up using photon-counting CT and artificial intelligence. Diagn Interv Imaging. 2024 Jul-Aug;105(7-8):273-280. doi: 10.1016/j.diii.2024.01.010. Epub 2024 Feb 16. PMID: 38368176.
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Mehier B, Mahmoudi K, Veugeois A, Masri A, Amabile N, Giudice CD, Paul JF. Diagnostic performance of deep learning to exclude coronary stenosis on CT angiography in TAVI patients. Int J Cardiovasc Imaging. 2024 May;40(5):981-990. doi: 10.1007/s10554-024-03063-5. Epub 2024 Mar 10. PMID: 38461472.
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Brendel JM, Walterspiel J, Hagen F, Kübler J, Brendlin AS, Afat S, Paul JF, Küstner T, Nikolaou K, Gawaz M, Greulich S, Krumm P, Winkelmann MT. Coronary artery disease detection using deep learning and ultrahigh-resolution photon-counting coronary CT angiography. Diagn Interv Imaging. 2024 Oct 3:S2211-5684(24)00209-2. doi: 10.1016/j.diii.2024.09.012. Epub ahead of print. PMID: 39366836.
Regulatory
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