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Author:

Feng, Yili (Feng, Yili.) | Fu, Ruisen (Fu, Ruisen.) | Sun, Hao (Sun, Hao.) | Wang, Xue (Wang, Xue.) | Yang, Yang (Yang, Yang.) | Wen, Chuanqi (Wen, Chuanqi.) | Hao, Yaodong (Hao, Yaodong.) | Sun, Yutong (Sun, Yutong.) | Li, Bao (Li, Bao.) | Li, Na (Li, Na.) | Yang, Haisheng (Yang, Haisheng.) | Feng, Quansheng (Feng, Quansheng.) | Liu, Jian (Liu, Jian.) | Liu, Zhuo (Liu, Zhuo.) | Zhang, Liyuan (Zhang, Liyuan.) | Liu, Youjun (Liu, Youjun.) (Scholars:刘有军)

Indexed by:

EI Scopus SCIE

Abstract:

Background and objective: Recently, computational fluid dynamics enables the non-invasive calculation of fractional flow reserve (FFR) based on 3D coronary model, but it is time-consuming. Currently, machine learning technique has emerged as an efficient and reliable approach for prediction, which allows saving a lot of analysis time. This study aimed at developing a simplified FFR prediction model for rapid and accurate assessment of functional significance of stenosis.Methods: A reduced-order lumped parameter model (LPM) of coronary system and cardiovascular system was constructed for rapidly simulating coronary flow, in which a machine learning model was embedded for accurately predicting stenosis flow resistance at a given flow from anatomical features of stenosis. Importantly, the LPM was personalized in both structures and parameters according to coronary geometries from computed tomography angiography and physiological measurements such as blood pressure and cardiac output for personalized simulations of coronary pressure and flow. Coronary lesions with invasive FFR <= 0.80 were defined as hemodynamically significant.Results: A total of 91 patients (93 lesions) who underwent invasive FFR were involved in FFR derived from machine learning (FFRML) calculation. Of the 93 lesions, 27 lesions (29.0%) showed lesion-specific ischemia. The average time of FFRML simulation was about 10 min. On a per-vessel basis, the FFRML and FFR were significantly correlated (r = 0.86, p < 0.001). The diagnostic accuracy, sensitivity, specificity, positive predictive value and negative predictive value were 91.4%, 92.6%, 90.9%, 80.6% and 96.8%, respectively. The area under the receiver-operating characteristic curve of FFRML was 0.984.Conclusion: In this selected cohort of patients, the FFRML improves the computational efficiency and ensures the accuracy. The favorable performance of FFRML approach greatly facilitates its potential application in detecting hemodynamically significant coronary stenosis in future routine clinical practice.

Keyword:

Fractional flow reserve Numerical simulation Reduced-order model Stenotic flow resistance Machine learning

Author Community:

  • [ 1 ] [Feng, Yili]Beijing Univ Technol, Fac Environm & Life, Dept Biomed Engn, Beijing, Peoples R China
  • [ 2 ] [Fu, Ruisen]Beijing Univ Technol, Fac Environm & Life, Dept Biomed Engn, Beijing, Peoples R China
  • [ 3 ] [Sun, Hao]Beijing Univ Technol, Fac Environm & Life, Dept Biomed Engn, Beijing, Peoples R China
  • [ 4 ] [Wang, Xue]Beijing Univ Technol, Fac Environm & Life, Dept Biomed Engn, Beijing, Peoples R China
  • [ 5 ] [Yang, Yang]Beijing Univ Technol, Fac Environm & Life, Dept Biomed Engn, Beijing, Peoples R China
  • [ 6 ] [Wen, Chuanqi]Beijing Univ Technol, Fac Environm & Life, Dept Biomed Engn, Beijing, Peoples R China
  • [ 7 ] [Hao, Yaodong]Beijing Univ Technol, Fac Environm & Life, Dept Biomed Engn, Beijing, Peoples R China
  • [ 8 ] [Li, Bao]Beijing Univ Technol, Fac Environm & Life, Dept Biomed Engn, Beijing, Peoples R China
  • [ 9 ] [Li, Na]Beijing Univ Technol, Fac Environm & Life, Dept Biomed Engn, Beijing, Peoples R China
  • [ 10 ] [Yang, Haisheng]Beijing Univ Technol, Fac Environm & Life, Dept Biomed Engn, Beijing, Peoples R China
  • [ 11 ] [Zhang, Liyuan]Beijing Univ Technol, Fac Environm & Life, Dept Biomed Engn, Beijing, Peoples R China
  • [ 12 ] [Liu, Youjun]Beijing Univ Technol, Fac Environm & Life, Dept Biomed Engn, Beijing, Peoples R China
  • [ 13 ] [Wang, Xue]Tianjin First Cent Hosp, Dept Pathol, Tianjin, Peoples R China
  • [ 14 ] [Sun, Yutong]Peking Univ Peoples Hosp, Dept Cardiol, Beijing, Peoples R China
  • [ 15 ] [Liu, Jian]Peking Univ Peoples Hosp, Dept Cardiol, Beijing, Peoples R China
  • [ 16 ] [Li, Na]Shandong First Med Univ, Sch Radiol, Tai An 271016, Shandong, Peoples R China
  • [ 17 ] [Li, Na]Shandong Acad Med Sci, Tai An 271016, Shandong, Peoples R China
  • [ 18 ] [Feng, Quansheng]First Peoples Hosp Guangshui, Dept Cardiol, Guangshui 432700, Hubei, Peoples R China
  • [ 19 ] [Liu, Zhuo]Peking Univ, Peoples Hosp, Dept Radiol, Beijing, Peoples R China

Reprint Author's Address:

  • [Zhang, Liyuan]Beijing Univ Technol, Fac Environm & Life, Dept Biomed Engn, Beijing, Peoples R China;;[Liu, Youjun]Beijing Univ Technol, Fac Environm & Life, Dept Biomed Engn, Beijing, Peoples R China;;

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Source :

ARTIFICIAL INTELLIGENCE IN MEDICINE

ISSN: 0933-3657

Year: 2023

Volume: 147

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: 4

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