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Deep learning-based coronary computed tomography analysis to predict functionally significant coronary artery stenosis

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Abstract

Fractional flow reserve derived from coronary CT (FFR-CT) is a noninvasive physiological technique that has shown a good correlation with invasive FFR. However, the use of FFR-CT is restricted by strict application standards, and the diagnostic accuracy of FFR-CT analysis may potentially be decreased by severely calcified coronary arteries because of blooming and beam hardening artifacts. The aim of this study was to evaluate the utility of deep learning (DL)-based coronary computed tomography (CT) data analysis in predicting invasive fractional flow reserve (FFR), especially in cases with severely calcified coronary arteries. We analyzed 184 consecutive cases (241 coronary arteries) which underwent coronary CT and invasive coronary angiography, including invasive FFR, within a three-month period. Mean coronary artery calcium scores were 963 ± 1226. We evaluated and compared the vessel-based diagnostic accuracy of our proposed DL model and a visual assessment to evaluate functionally significant coronary artery stenosis (invasive FFR < 0.80). A deep neural network was trained with consecutive short axial images of coronary arteries on coronary CT. Ninety-one coronary arteries of 89 cases (48%) had FFR-positive functionally significant stenosis. On receiver operating characteristics (ROC) analysis to predict FFR-positive stenosis using the trained DL model, average area under the curve (AUC) of the ROC curve was 0.756, which was superior to the AUC of visual assessment of significant (≥ 70%) coronary artery stenosis on CT (0.574, P = 0.011). The sensitivity, specificity, positive and negative predictive value (PPV and NPV), and accuracy of the DL model and visual assessment for detecting FFR-positive stenosis were 82 and 36%, 68 and 78%, 59 and 48%, 87 and 69%, and 73 and 63%, respectively. Sensitivity and NPV for the prediction of FFR-positive stenosis were significantly higher with our DL model than visual assessment (P = 0.0004, and P = 0.024). DL-based coronary CT data analysis has a higher diagnostic accuracy for functionally significant coronary artery stenosis than visual assessment.

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Funding

This work was partially supported by The Public Foundation of Chubu Science and Technology Center.

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Author notes

  1. Manami Takahashi and Reika Kosuda have equally contributed to this work.

Authors and Affiliations

  1. Department of Cardiovascular Medicine, Chiba University Graduate School of Medicine, 1-8-1 Inohana, Chuo-ku, Chiba, Japan

    Manami Takahashi, Hiroyuki Takaoka, Hideki Kitahara, Masafumi Sugawara & Yoshio Kobayashi

  2. Graduate School of Science and Engineering, Chiba University, Chiba, Japan

    Reika Kosuda

  3. Department of Diagnostic Radiology and Radiation Oncology, Graduate School of Medicine, Chiba University, Chiba, Japan

    Hajime Yokota, Takuro Horikoshi & Takashi Uno

  4. Graduate School of Engineering, Chiba University, Chiba, Japan

    Yasukuni Mori & Hiroki Suyari

  5. Department of Radiology, Chiba University Hospital, Chiba, Japan

    Joji Ota

  6. Quantum-Medicine AI Research Group, QST Advanced Study Laboratory, National Institutes for Quantum and Radiological Science and Technology, Chiba, Japan

    Yasuhiko Tachibana

  7. Department of Cardiology, Eastern Chiba Medical Center, Togane, Japan

    Tomonori Kanaeda

Authors
  1. Manami Takahashi
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  2. Reika Kosuda
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Correspondence to Hiroyuki Takaoka.

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Takahashi, M., Kosuda, R., Takaoka, H. et al. Deep learning-based coronary computed tomography analysis to predict functionally significant coronary artery stenosis. Heart Vessels 38, 1318–1328 (2023). https://doi.org/10.1007/s00380-023-02288-z

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  • DOI: https://doi.org/10.1007/s00380-023-02288-z

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