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Author:

Liu, Jincheng (Liu, Jincheng.) | Li, Bao (Li, Bao.) | Yang, Yang (Yang, Yang.) | Huang, Suqin (Huang, Suqin.) | Sun, Hao (Sun, Hao.) | Liu, Jian (Liu, Jian.) | Liu, Youjun (Liu, Youjun.) (Scholars:刘有军)

Indexed by:

EI Scopus SCIE

Abstract:

The underuse of invasive fractional flow reserve (FFR) in clinical practice has motivated research towards noninvasive prediction of FFR. Although the non-invasive derivation of FFR (FFRCT) using computational fluid dynamics (CFD) principles has become a common practice, its clinical application has been limited due to the considerable time required for computation of resulting changes in haemodynamic conditions. An alternative to CFD technology is incorporating a neural network into the computational process to reduce the time necessary for running an effective model. In this study we propose a cascade of data -driven and physic -based neural networks (DP -NN) for predicting FFR (DL-FFRCT). The first network of cascade network DP -NN includes geometric features, and the second network includes physical features. We compare the differences between data -driven neural network (D -NN) and DP -NN for predicting FFR. The training and testing datasets were obtained by solving the three-dimensional incompressible Navier-Stokes equations. Coronary flow and geometric features were used as inputs to train D NN. In DP -NN the training process involves first training a D -NN to output resting Delta P as one input feature to the DP -NN. Secondly, the physics -based microcirculatory resistance as another input feature to the DP -NN. Using clinically measured FFR as the "gold standard", we validated the computational accuracy of DL-FFRCT in 77 patients. Compared to D -NN, DP -NN improved the prediction of Delta P (R2 = 0.87 vs. R2 = 0.92). Statistical analysis demonstrated that the diagnostic accuracy of DL-FFRCT was not inferior to FFRCT (85.71 % vs. 88.3 %) and the computational time was reduced by a factor of approximately 3000 (4.26 s vs. 3.5 h). DP -NN represents a near real-time, interpretable, and highly accurate deep -learning network, which contributes to the development of high-performance computational methods for haemodynamics. We anticipate that DP -NN will enable near real-time prediction of DL-FFRCT in personalized narrow blood vessels and provide guidance for cardiovascular disease treatments.

Keyword:

Deep learning Computational FFR Coronary artery disease Cascade neural networks

Author Community:

  • [ 1 ] [Liu, Jincheng]Beijing Univ Technol, Coll Chem & Life Sci, Dept Biomed Engn, Beijing, Peoples R China
  • [ 2 ] [Li, Bao]Beijing Univ Technol, Coll Chem & Life Sci, Dept Biomed Engn, Beijing, Peoples R China
  • [ 3 ] [Yang, Yang]Beijing Univ Technol, Coll Chem & Life Sci, Dept Biomed Engn, Beijing, Peoples R China
  • [ 4 ] [Huang, Suqin]Beijing Univ Technol, Coll Chem & Life Sci, Dept Biomed Engn, Beijing, Peoples R China
  • [ 5 ] [Sun, Hao]Beijing Univ Technol, Coll Chem & Life Sci, Dept Biomed Engn, Beijing, Peoples R China
  • [ 6 ] [Liu, Youjun]Beijing Univ Technol, Coll Chem & Life Sci, Dept Biomed Engn, Beijing, Peoples R China
  • [ 7 ] [Liu, Jian]Peking Univ Peoples Hosp, Cardiovasc Dept, Beijing, Peoples R China
  • [ 8 ] [Liu, Youjun]Beijing Univ Technol, Coll Chem & Life Sci, Dept Biomed Engn, Beijing 100124, Peoples R China

Reprint Author's Address:

  • 刘有军

    [Liu, Youjun]Beijing Univ Technol, Coll Chem & Life Sci, Dept Biomed Engn, Beijing 100124, Peoples R China

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Source :

COMPUTERS IN BIOLOGY AND MEDICINE

ISSN: 0010-4825

Year: 2024

Volume: 169

7 . 7 0 0

JCR@2022

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 7

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 5

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