Diagnostic Accuracy of a Machine-Learning Approach to Coronary Computed Tomographic Angiography–Based Fractional Flow Reserve

Author:

Coenen Adriaan12,Kim Young-Hak3,Kruk Mariusz4,Tesche Christian5,De Geer Jakob6,Kurata Akira27,Lubbers Marisa L.12,Daemen Joost1,Itu Lucian8,Rapaka Saikiran9,Sharma Puneet9,Schwemmer Chris10,Persson Anders6,Schoepf U. Joseph5,Kepka Cezary4,Hyun Yang Dong11,Nieman Koen1212

Affiliation:

1. Department of Cardiology (A.C., M.L.L., J.D., K.N.)

2. Department of Radiology (A.C., A.K., M.L.L., K.N.)

3. Erasmus University Medical Center, Rotterdam, the Netherlands. Department of Cardiology, Heart Institute (Y.-H.K.)

4. Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea. Coronary Disease and Structural Heart Diseases Department, Institute of Cardiology, Warsaw, Poland (M.K., C.K.).

5. Division of Cardiovascular Imaging, Medical University of South Carolina, Charleston (C.T., U.J.S.).

6. Department of Radiology and Department of Medical and Health Sciences, Center for Medical Image Science and Visualization, Linköping University, Sweden (J.D.G., A.P.).

7. Department of Radiology, Ehime University Graduate School of Medicine, Japan (A.K.).

8. Corporate Technology, Siemens SRL, Brasov, Romania (L.I.).

9. Medical Imaging Technologies, Siemens Healthcare, Princeton, NJ (S.R., P.S.).

10. Computed Tomography-Research & Development, Siemens Healthcare GmbH, Forchheim, Germany (C.S.).

11. Department of Radiology (D.H.Y.)

12. Stanford University School of Medicine, Cardiovascular Institute, Stanford, CA, USA (K.N.).

Publisher

Ovid Technologies (Wolters Kluwer Health)

Subject

Cardiology and Cardiovascular Medicine,Radiology, Nuclear Medicine and imaging

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