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Diagnostic Performance of Deep Learning and Computational Fluid Dynamics-based Instantaneous Wave-free Ratio Derived from Computed Tomography Angiography by Jingyuan Zhang; Kun Xu; Yumeng Hu; Lin Yang; Xiaochang Leng; Hongfeng Jin; Yiming Tang; Xiaowei Liu; Chen Ye; Yitao Guo; Lei Wang; Jianjun Zhang; Yue Feng; Caiyun Mou; Lijiang Tang; Jianping Xiang; Changqing Du is a Medicine article available to read on EtoBox.

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## Abstract ## Background and objectives Both fractional flow reserve (FFR) and instantaneous wave-free ratio (iFR) are widely used to evaluate ischemia-causing coronary lesions. A new method of CT-iFR, namely AccuiFRct, for calculating iFR based on deep learning and computational fluid dynamics (CFD) using coronary computed tomography angiography (CCTA) has been proposed. In this study, the diagnostic performance of AccuiFRct was thoroughly assessed using iFR as the reference standard. ## Methods Data of a total of 36 consecutive patients with 36 vessels from a single-center who underwent CCTA, invasive FFR, and iFR were retrospectively analyzed. The CT-derived iFR values were computed using a novel deep learning and CFD-based model. ## Results Mean values of FFR and iFR were 0.80 ± 0.10 and 0.91 ± 0.06, respectively. AccuiFRct was well correlated with FFR and iFR (correlation coefficients, 0.67 and 0.68, respectively). The diagnostic accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of AccuiFRct ≤ 0.89 for predicting FFR ≤ 0.80 were 78%, 73%, 81%, 73%, and 81%, respectively. Those of AccuiFRct ≤ 0.89 for predicting iFR ≤ 0.89 were 81%, 7

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Author

Jingyuan Zhang; Kun Xu; Yumeng Hu; Lin Yang; Xiaochang Leng; Hongfeng Jin; Yiming Tang; Xiaowei Liu; Chen Ye; Yitao Guo; Lei Wang; Jianjun Zhang; Yue Feng; Caiyun Mou; Lijiang Tang; Jianping Xiang; Changqing Du

Publisher

Springer Science and Business Media LLC

Published

2022

Language

EN

Field

Medicine (Health Sciences)