Machine learning and computational fluid dynamics derived FFRCT demonstrate comparable diagnostic performance in patients with coronary artery disease; A Systematic Review and Meta-Analysis
Narimani-Javid R, Moradi M, Mahalleh M, Najafi-Vosough R, Arzhangzadeh A, Khalique O, Mojibian H, Kuno T, Mohsen A, Alam M, Shafiei S, Khansari N, Shaghaghi Z, Nozhat S, Hosseini K, Hosseini S. Machine learning and computational fluid dynamics derived FFRCT demonstrate comparable diagnostic performance in patients with coronary artery disease; A Systematic Review and Meta-Analysis. Journal Of Cardiovascular Computed Tomography 2025 PMID: 39988511, DOI: 10.1016/j.jcct.2025.02.004.Peer-Reviewed Original ResearchArea under the curveDiagnostic odds ratioDiagnostic performanceComputed tomography-derived fractional flow reserveDiagnostic performance of FFRCTHemodynamically significant coronary artery stenosisSignificant coronary artery stenosisMeta-analysisPer-patient levelReceiver operating characteristic curveCoronary artery diseaseCoronary artery stenosisPer-vessel levelFractional flow reserveNoninvasive diagnostic techniquesMachine learningInvasive FFRPooled specificityArtery diseaseArtery stenosisFlow reserveOdds ratioCochrane LibraryFFRCTPatients
This site is protected by hCaptcha and its Privacy Policy and Terms of Service apply