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. 2019 Jan;16(1):42-48.
doi: 10.11909/j.issn.1671-5411.2019.01.010.

Diagnostic accuracy of a deep learning approach to calculate FFR from coronary CT angiography

Zhi-Qiang Wang  1 , Yu-Jie Zhou  1 , Ying-Xin Zhao  1 , Dong-Mei Shi  1 , Yu-Yang Liu  1 , Wei Liu  1 , Xiao-Li Liu  1 , Yue-Ping Li  1
Affiliations

Diagnostic accuracy of a deep learning approach to calculate FFR from coronary CT angiography

Zhi-Qiang Wang et al. J Geriatr Cardiol. 2019 Jan.

Abstract

Background: The computational fluid dynamics (CFD) approach has been frequently applied to compute the fractional flow reserve (FFR) using computed tomography angiography (CTA). This technique is efficient. We developed the DEEPVESSEL-FFR platform using the emerging deep learning technique to calculate the FFR value out of CTA images in five minutes. This study is to evaluate the DEEPVESSEL-FFR platform using the emerging deep learning technique to calculate the FFR value from CTA images as an efficient method.

Methods: A single-center, prospective study was conducted and 63 patients were enrolled for the evaluation of the diagnostic performance of DEEPVESSEL-FFR. Automatic quantification method for the three-dimensional coronary arterial geometry and the deep learning based prediction of FFR were developed to assess the ischemic risk of the stenotic coronary arteries. Diagnostic performance of the DEEPVESSEL-FFR was assessed by using wire-based FFR as reference standard. The primary evaluation factor was defined by using the area under receiver-operation characteristics curve (AUC) analysis.

Results: For per-patient level, taking the cut-off value ≤ 0.8 referring to the FFR measurement, DEEPVESSEL-FFR presented higher diagnostic performance in determining ischemia-related lesions with area under the curve of 0.928 compare to CTA stenotic severity 0.664. DEEPVESSEL-FFR correlated with FFR (R = 0.686, P < 0.001), with a mean difference of -0.006 ± 0.0091 (P = 0.619). The secondary evaluation factors, indicating per vessel accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were 87.3%, 97.14%, 75%, 82.93%, and 95.45%, respectively.

Conclusion: DEEPVESSEL-FFR is a novel method that allows efficient assessment of the functional significance of coronary stenosis.

Keywords: Computed tomography angiography; Coronary artery; Deep learning; Fractional flow reserve.

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Figures

Figure 1.
Figure 1.. The process of DEEPVESSEL-FFR.
MLNN: multilevel neural network; RNN: multilevel neural network.
Figure 2.
Figure 2.. Representative examples of subjects from the study.
(A): Multiplanar reformat of coronary computed tomography (CT) angiogram; (B): the left anterior descending artery (blue arrow) and DEEPVESSEL-FFR 0.81; (C): invasive coronary angiogram; and (D): invasive FFR measurement, a measured FFR value of 0.81. FFR: fractional flow reserve.
Figure 3.
Figure 3.. Correlation (A & C) and agreement (B & D) analysis on a per-patient level and per-vessel level.
DVFFR: DEEPVESSEL fractional flow reserve.
Figure 4.
Figure 4.. AUC of DEEPVESSEL-FFR vs. coronary CTA for demonstration of ischemia (FFR 0.80) on a per-patient and per-vessel basis.
AUC: area under receiver-operation characteristics curve; CTA: computed tomography angiography; FFR: fractional flow reserve.
See this image and copyright information in PMC

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