Please Login >
English| 简体中文

  • Complex
  • Title
  • Keyword
  • Abstract
  • Scholars
  • Journal
  • ISSN
  • Conference
搜索

Advanced Search

Author：

Feng, Y. (Feng, Y..) | Fu, R. (Fu, R..) | Sun, H. (Sun, H..) | Wang, X. (Wang, X..) | Yang, Y. (Yang, Y..) | Wen, C. (Wen, C..) | Hao, Y. (Hao, Y..) | Sun, Y. (Sun, Y..) | Li, B. (Li, B..) | Li, N. (Li, N..) | Yang, H. (Yang, H..) | Feng, Q. (Feng, Q..) | Liu, J. (Liu, J..) | Liu, Z. (Liu, Z..) | Zhang, L. (Zhang, L..) | Liu, Y. (Liu, Y..)

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.　©　2023　Elsevier　B.V.

Keyword：

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

Author Community：

  • [ 1 ] [Feng Y.]Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing, 100124, China
  • [ 2 ] [Fu R.]Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing, 100124, China
  • [ 3 ] [Sun H.]Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing, 100124, China
  • [ 4 ] [Wang X.]Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing, 100124, China
  • [ 5 ] [Wang X.]Department of Equipment and Materials, Tianjin First Central Hospital, Tianjin, China
  • [ 6 ] [Yang Y.]Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing, 100124, China
  • [ 7 ] [Wen C.]Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing, 100124, China
  • [ 8 ] [Hao Y.]Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing, 100124, China
  • [ 9 ] [Sun Y.]Department of Cardiology, Peking University People's Hospital, Beijing, China
  • [ 10 ] [Li B.]Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing, 100124, China
  • [ 11 ] [Li N.]Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing, 100124, China
  • [ 12 ] [Li N.]School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Shandong, Tai'an, 271016, China
  • [ 13 ] [Yang H.]Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing, 100124, China
  • [ 14 ] [Feng Q.]Department of Cardiology, The First People's Hospital of Guangshui, Hubei, 432700, China
  • [ 15 ] [Liu J.]Department of Cardiology, Peking University People's Hospital, Beijing, China
  • [ 16 ] [Liu Z.]Department of Radiology, Peking University People's Hospital, Beijing, China
  • [ 17 ] [Zhang L.]Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing, 100124, China
  • [ 18 ] [Liu Y.]Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing, 100124, China

Reprint Author's Address：

Email：

Show more details

Related Keywords：

Related Article：

Source ：

Artificial Intelligence in Medicine

ISSN： 0933-3657

Year： 2024

Volume： 147

Cited Count：

WoS CC Cited Count： 0

SCOPUS Cited Count： 3

ESI Highly Cited Papers on the List： 0 Unfold All

WanFang Cited Count：

Chinese Cited Count：

30 Days PV： 4

Affiliated Colleges：

Get Fulltext

DOI Library Discovery Baidu Scholar Search SCOPUS
Online/Total：521/10591986
Address：BJUT Library(100 Pingleyuan,Chaoyang District,Beijing 100124, China Post Code：100124) Contact Us：010-67392185
Copyright：BJUT Library Technical Support：Beijing Aegean Software Co., Ltd.