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: Journal of cardiovascular computed tomography
PMID: 39988511

Abstract

BACKGROUND: As a new noninvasive diagnostic technique, computed tomography-derived fraction flow reserve (FFRCT) has been used to identify hemodynamically significant coronary artery stenosis. FFRCT can be calculated using computational fluid dynamics (CFD) or machine learning (ML) approaches. It was hypothesized that ML-based FFRCT (FFRCT) has comparable diagnostic performance with CFD-based FFRCT (FFRCT). We used invasive FFR as the reference test to evaluate the diagnostic performance of FFRCT vs. FFRCT.

Authors

  • Roozbeh Narimani-Javid
    Research Center for Advanced Technologies in Cardiovascular Medicine, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran. Electronic address: Roozbehnarimani@gmail.com.
  • Mehdi Moradi
    IBM Almaden Research Center, San Jose, CA.
  • Mehrdad Mahalleh
    Tehran Heart Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran. Electronic address: Mahallehm80@gmail.com.
  • Roya Najafi-Vosough
    Department of Biostatistics, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran.
  • Alireza Arzhangzadeh
    Department of Cardiology, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran. Electronic address: Alirezaarjang@gmail.com.
  • Omar Khalique
    Seymour, Paul, and Gloria Milstein Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center and NewYork-Presbyterian Hospital, New York, New York, USA.
  • Hamid Mojibian
    Department of Radiology & Biomedical Imaging, Section of Vascular & Interventional Radiology, Yale School of Medicine, New Haven, CT, USA.
  • Toshiki Kuno
    Department of Medicine, Mount Sinai Beth Israel, New York, New York, USA.
  • Amr Mohsen
    Division of Cardiology, Loma Linda University Medical Center, Loma Linda, CA, USA. Electronic address: amohsen@llu.edu.
  • Mahboob Alam
    National Institute of Animal Science, Rural Development Administration, Cheonan 31000, Chungcheongnam-do, Republic of Korea.
  • Sasan Shafiei
    Department of Cardiology, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran. Electronic address: sasan.shafee@gmail.com.
  • Nakisa Khansari
    Department of Cardiology, School of Medicine, Hamadan University of Medical Sciences, Hamadan, Iran. Electronic address: N_kh_80@yahoo.com.
  • Zahra Shaghaghi
    Cardiovascular Research Center, Hamadan University of Medical Science, Hamadan, Iran. Electronic address: z.shaghaghi90@yahoo.com.
  • Salma Nozhat
    Department of Cardiology, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran. Electronic address: Salmanozhat1989@gmail.com.
  • Kaveh Hosseini
    Cardiac Primary Prevention Research Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran Heart Center, Kargar St. Jalal al-Ahmad Cross, 1411713138, Tehran, Iran.
  • Seyed Kianoosh Hosseini
    Department of Cardiology, School of Medicine, Hamadan University of Medical Sciences, Hamadan, Iran. Electronic address: k.hoseini86@gmail.com.

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