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

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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.

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Abbreviations

BMI:

Body mass index

CAD:

coronary artery disease

CAD-RADS:

Coronary artery disease-reporting and data system

CCTA:

Coronary computed tomography angiography

CT:

Computed tomography

CX:

Circumflex artery

DLM:

Deep learning model

ECG:

Electrocardiogram

ICA:

Invasive coronary angiography

LAD:

Left anterior descending artery

LVEF:

Left ventricular ejection fraction

MPR:

Multiplanar reformation

NPV:

Negative predictive value

PPV:

Positive predictive value

RCA:

Right coronary artery

TAVI:

Transcatheter aortic valve implantation

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Authors and Affiliations

  1. Department of Radiology, Cardiac Imaging, Institut Mutualiste Montsouris, 75014, Paris, France

    Benjamin Mehier

  2. Interventional Cardiology Department, Institut Mutualiste Montsouris, 75014, Paris, France

    Khalil Mahmoudi, Aurélie Veugeois, Alaa Masri & Nicolas Amabile

  3. Radiology and Interventional Radiology Department, Cardiac Imaging, Institut Mutualiste Montsouris, 75014, Paris, France

    Costantino Del Giudice

  4. Department of Radiology, Cardiac Imaging, Institut Mutualiste Montsouris, Spimed-AI, 75014, Paris, France

    Jean-François Paul

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Contributions

B.M. study design; collected data ; wrote the main manuscript ; prepared figures and tables ; reviewed the manuscript K.M. ; A.M. ; N.A. ; A.V. study design; collected data ; reviewed the manuscript C.DG. performed the statistical analysis ; reviewed the manuscript JF.P. ; reviewed the manuscript.

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Correspondence to Benjamin Mehier.

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Dr Jean-François Paul is founder and CEO of Spimed-AI.

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Mehier, B., Mahmoudi, K., Veugeois, A. et al. Diagnostic performance of deep learning to exclude coronary stenosis on CT angiography in TAVI patients. Int J Cardiovasc Imaging (2024). https://doi.org/10.1007/s10554-024-03063-5

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