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Machine Learning and Computational Fluid Dynamics Derived Ffrct Demonstrate Comparable Diagnostic Performance in Patients With Coronary Artery Disease; a Systematic Review and Meta-Analysis Publisher Pubmed



Narimanijavid R1 ; Moradi M2 ; Mahalleh M3 ; Najafivosough R4 ; Arzhangzadeh A5 ; Khalique O6 ; Mojibian H7 ; Kuno T8 ; Mohsen A9 ; Alam M10 ; Shafiei S5 ; Khansari N2 ; Shaghaghi Z11 ; Nozhat S5 Show All Authors
Authors
  1. Narimanijavid R1
  2. Moradi M2
  3. Mahalleh M3
  4. Najafivosough R4
  5. Arzhangzadeh A5
  6. Khalique O6
  7. Mojibian H7
  8. Kuno T8
  9. Mohsen A9
  10. Alam M10
  11. Shafiei S5
  12. Khansari N2
  13. Shaghaghi Z11
  14. Nozhat S5
  15. Hosseini K3
  16. Hosseini SK2
Show Affiliations
Authors Affiliations
  1. 1. Research Center for Advanced Technologies in Cardiovascular Medicine, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
  2. 2. Department of Cardiology, School of Medicine, Hamadan University of Medical Sciences, Hamadan, Iran
  3. 3. Tehran Heart Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
  4. 4. Research Center for Health Sciences, Hamadan University of Medical Sciences, Hamadan, Iran
  5. 5. Department of Cardiology, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
  6. 6. Department of Cardiology, St. Francis Hospital, Roslyn, NY, United States
  7. 7. Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States
  8. 8. Cardiology Division, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
  9. 9. Division of Cardiology, Loma Linda University Medical Center, Loma Linda, CA, United States
  10. 10. Division of Cardiology, Baylor College of Medicine, Houston, TX, United States
  11. 11. Cardiovascular Research Center, Hamadan University of Medical Science, Hamadan, Iran

Source: Journal of Cardiovascular Computed Tomography Published:2025


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 (FFRCTML) has comparable diagnostic performance with CFD-based FFRCT (FFRCTCFD). We used invasive FFR as the reference test to evaluate the diagnostic performance of FFRCTML vs. FFRCTCFD. Methods: We searched PubMed, Cochrane Library, EMBASE, WOS, and Scopus for articles published until March 2024. We analyzed the synthesized sensitivity, specificity, and diagnostic odds ratio (DOR) of FFRCTML vs FFRCTCFD at both the patient and vessel levels. We generated summary receiver operating characteristic curves (SROC) and then calculated the area under the curve (AUC). Results: This meta-analysis included 23 studies reporting FFRCTCFD diagnostic performance and 18 studies reporting FFRCTML diagnostic performance. In the FFRCTCFD group, 2501 patients and 3764 vessels or lesions were analyzed. In the FFRCTML group, 1323 patients and 4194 vessels or lesions were analyzed. Our results showed that at the per-patient level, FFRCTCFD and FFRCTML had comparable pooled specificity (Z ​= ​−0.59, P ​= ​0.55) and AUC (P ​= ​0.5). At the per-vessel level, FFRCTCFD and FFRCTML also showed comparable specificity (Z ​= ​0.94, P ​= ​0.34), DOR (Z ​= ​0.7, P ​= ​0.48), and AUC (P ​= ​0.74). However, the sensitivity of FFRCTML was significantly lower compared to FFRCTCFD at both patient (Z ​= ​−3.85, P ​= ​0.0001) and vessel (Z ​= ​−2.05, P ​= ​0.04) levels. Conclusion: The FFRCTML technique was comparable to standard CFD approaches in terms of AUC and specificity. However, it did not achieve the same level of sensitivity as FFRCTCFD. © 2025 Society of Cardiovascular Computed Tomography
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