Diagnostic performance of deep learning to exclude coronary stenosis on CT angiography in TAVI patients

Benjamin Mehier; Khalil Mahmoudi; Aurélie Veugeois; Alaa Masri; Nicolas Amabile; Costantino Del Giudice; Jean-François Paul

doi:10.1007/s10554-024-03063-5

Diagnostic performance of deep learning to exclude coronary stenosis on CT angiography in TAVI patients

Int J Cardiovasc Imaging. 2024 Mar 10. doi: 10.1007/s10554-024-03063-5. Online ahead of print.

Authors

Benjamin Mehier¹, Khalil Mahmoudi², Aurélie Veugeois², Alaa Masri², Nicolas Amabile², Costantino Del Giudice³, Jean-François Paul⁴

Affiliations

¹ Department of Radiology, Cardiac Imaging, Institut Mutualiste Montsouris, 75014, Paris, France. benjamin.mehier@imm.fr.
² Interventional Cardiology Department, Institut Mutualiste Montsouris, 75014, Paris, France.
³ Radiology and Interventional Radiology Department, Cardiac Imaging, Institut Mutualiste Montsouris, 75014, Paris, France.
⁴ Department of Radiology, Cardiac Imaging, Institut Mutualiste Montsouris, Spimed-AI, 75014, Paris, France.

PMID: 38461472
DOI: 10.1007/s10554-024-03063-5

Abstract

We evaluated the diagnostic performance of a deep-learning model (DLM) (CorEx®, Spimed-AI, Paris, France) designed to automatically detect > 50% coronary stenosis on coronary computed tomography angiography (CCTA) images. We studied inter-observer variability as an additional aim. CCTA images obtained before transcatheter aortic valve implantation (TAVI) were assessed by two radiologists and the DLM, and the results were compared to those of invasive coronary angiography (ICA) used as the reference standard. 165 consecutive patients underwent both CCTA and ICA as part of their TAVI work-up. We excluded the 42 (25.5%) patients with a history of stenting or bypass grafting and the 23 (13.9%) patients with low-quality images. We retrospectively subjected the CCTA images from the remaining 100 patients to evaluation by the DLM and compared the DLM and ICA results. All 25 patients with > 50% stenosis by ICA also had > 50% stenosis by DLM evaluation of CCTA: thus, the DLM had 100% sensitivity and 100% negative predictive value. False-positive DLM results were common, yielding a positive predictive value of only 39% (95% CI, 27-51%). Two radiologists with 3 and 25 years' experience, respectively, performed similarly to the DLM in evaluating the CCTA images; thus, accuracy did not differ significantly between each reader and the DLM (p = 0.625 and p = 0.375, respectively). The DLM had 100% negative predictive value for > 50% stenosis and performed similarly to experienced radiologists. This tool may hold promise for identifying the up to one-third of patients who do not require ICA before TAVI.

Keywords: Artificial intelligence; Coronary arteries; Coronary artery disease; Coronary computed tomography angiography; Deep learning classification; Tomography angiography.