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

Journal: The international journal of cardiovascular imaging
Published Date: May 1, 2024

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.

Authors

  • Benjamin Mehier
    Department of Radiology, Cardiac Imaging, Institut Mutualiste Montsouris, 75014, Paris, France. benjamin.mehier@imm.fr.
  • Khalil Mahmoudi
    Interventional Cardiology Department, Institut Mutualiste Montsouris, 75014, Paris, France.
  • Aurélie Veugeois
    Cardiology Department, Institut Mutualiste Montsouris, Paris, France.
  • Alaa Masri
    Interventional Cardiology Department, Institut Mutualiste Montsouris, 75014, Paris, France.
  • Nicolas Amabile
    Cardiology Department, Institut Mutualiste Montsouris, Paris, France.
  • Costantino Del Giudice
    Radiology and Interventional Radiology Department, Cardiac Imaging, Institut Mutualiste Montsouris, 75014, Paris, France.
  • Jean-François Paul
    Institut Mutualiste Montsouris : 42 Boulevard Jourdan, 75014 Paris. Electronic address: dr.jfpaul@gmail.com.

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