AAPM task group report 273: Recommendations on best practices for AI and machine learning for computer‐aided diagnosis in medical imaging-Reference-Cited by-同舟云学术

AAPM task group report 273: Recommendations on best practices for AI and machine learning for computer‐aided diagnosis in medical imaging

Published:2023-01-06 Issue:2 Volume:50 Page:
ISSN:0094-2405
Container-title:Medical Physics
language:en
Short-container-title:Medical Physics

Author:

Hadjiiski Lubomir¹,Cha Kenny²,Chan Heang‐Ping¹,Drukker Karen³,Morra Lia⁴,Näppi Janne J.⁵,Sahiner Berkman²,Yoshida Hiroyuki⁵,Chen Quan⁶,Deserno Thomas M.⁷,Greenspan Hayit⁸,Huisman Henkjan⁹,Huo Zhimin¹⁰,Mazurchuk Richard¹¹,Petrick Nicholas²,Regge Daniele¹²¹³,Samala Ravi²,Summers Ronald M.¹⁴,Suzuki Kenji¹⁵,Tourassi Georgia¹⁶,Vergara Daniel¹⁷,Armato Samuel G.³

Affiliation:

1. Department of Radiology University of Michigan Ann Arbor Michigan USA

2. U.S. Food and Drug Administration Silver Spring Maryland USA

3. Department of Radiology University of Chicago Chicago Illinois USA

4. Department of Control and Computer Engineering Politecnico di Torino Torino Italy

5. 3D Imaging Research Department of Radiology Massachusetts General Hospital and Harvard Medical School Boston Massachusetts USA

6. Department of Radiation Medicine University of Kentucky Lexington Kentucky USA

7. Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School Braunschweig Germany

8. Department of Biomedical Engineering Faculty of Engineering Tel Aviv, Israel & Department of Radiology Ichan School of Medicine Tel Aviv University, Mt Sinai, New York New York USA

9. Radboud Institute for Health Sciences Radboud University Medical Center Nijmegen The Netherlands

10. Tencent America Palo Alto California USA

11. Division of Cancer Prevention National Cancer Institute National Institutes of Health Bethesda Maryland USA

12. Radiology Unit Candiolo Cancer Institute, FPO‐IRCCS Candiolo Italy

13. Department of Surgical Sciences University of Turin Turin Italy

14. Radiology and Imaging Sciences National Institutes of Health Clinical Center Bethesda Maryland USA

15. Institute of Innovative Research Tokyo Institute of Technology Tokyo Japan

16. Oak Ridge National Lab Oak Ridge Tennessee USA

17. Department of Radiology Yale New Haven Hospital New Haven Connecticut USA

Publisher

Wiley

Subject

General Medicine

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/mp.16188

Reference194 articles.

1. Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans

2. Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies;Nagendran M;Bmj‐British Medical Journal,2020

3. Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis

4. Design Characteristics of Studies Reporting the Performance of Artificial Intelligence Algorithms for Diagnostic Analysis of Medical Images: Results from Recently Published Papers

5. Evaluation of computer-aided detection and diagnosis systemsa)

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