RSNA-MICCAI Panel Discussion: Machine Learning for Radiology from Challenges to Clinical Applications.

Artificial Neural Network Algorithms Back-Propagation Machine Learning Algorithms

Journal

Radiology. Artificial intelligence
ISSN: 2638-6100
Titre abrégé: Radiol Artif Intell
Pays: United States
ID NLM: 101746556

Informations de publication

Date de publication:
Sep 2021
Historique:
received: 06 05 2021
revised: 30 06 2021
accepted: 12 07 2021
entrez: 7 10 2021
pubmed: 8 10 2021
medline: 8 10 2021
Statut: epublish

Résumé

On October 5, 2020, the Medical Image Computing and Computer Assisted Intervention Society (MICCAI) 2020 conference hosted a virtual panel discussion with members of the Machine Learning Steering Subcommittee of the Radiological Society of North America. The MICCAI Society brings together scientists, engineers, physicians, educators, and students from around the world. Both societies share a vision to develop radiologic and medical imaging techniques through advanced quantitative imaging biomarkers and artificial intelligence. The panel elaborated on how collaborations between radiologists and machine learning scientists facilitate the creation and clinical success of imaging technology for radiology. This report presents structured highlights of the moderated dialogue at the panel.

Identifiants

DOI: 10.1148/ryai.2021210118 PMID: 34617032 PMC: PMC8489458
pubmed: 34617032
doi: 10.1148/ryai.2021210118
pmc: PMC8489458
doi:

Types de publication

Journal Article

Langues

eng

Pagination

e210118

Informations de copyright

2021 by the Radiological Society of North America, Inc.

Déclaration de conflit d'intérêts

Disclosures of Conflicts of Interest: J.M. institution received grant from GE Healthcare; author receives royalties from GE Healthcare for licensing of pneumothorax detection algorithm; author paid for development of educational presentations from UCSF Postgraduate Medical Education; author’s spouse has been employed by AbbVie and Annexon Biosciences; author is associate editor of Radiology: Artificial Intelligence. J.K.C. institution received grants from GE, NIH, NSF, and Genentech; author received travel accommodations from IBM; author is deputy editor of Radiology: Artificial Intelligence. A.F. disclosed no relevant relationships. M.G.L. author is consultant to the National Institutes of Health for grant review; author is co-founder of PediaMetrix; author’s institution received grants from National Institutes of Health, National Science Foundation, Department of Defense, and Philips Healthcare; author has stock/stock options in PediaMetrix.

Références

PLoS Med. 2018 Nov 20;15(11):e1002697
pubmed: 30457991
Front Neurol. 2019 Aug 14;10:869
pubmed: 31474928
Radiol Artif Intell. 2020 Mar 18;2(2):e190111
pubmed: 33937819

Auteurs

John Mongan (J)

Department of Radiology and Biomedical Imaging and Center for Intelligent Imaging, University of California San Francisco, 505 Parnassus Ave, Room M-391, San Francisco, CA 94143 (J.M.); Department of Radiology and MGH and BWH Center for Clinical Data Science, Massachusetts General Hospital, Boston, Mass (J.K.C.); Department of Radiology, Thomas Jefferson University Hospital, Philadelphia, Pa (A.F.); Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Hospital, Washington, DC (M.G.L.); and Departments of Pediatrics and Radiology, George Washington University School of Medicine, Washington, DC (M.G.L.).
ORCID: 0000-0003-2765-7451

Jayashree Kalpathy-Cramer (J)

Department of Radiology and Biomedical Imaging and Center for Intelligent Imaging, University of California San Francisco, 505 Parnassus Ave, Room M-391, San Francisco, CA 94143 (J.M.); Department of Radiology and MGH and BWH Center for Clinical Data Science, Massachusetts General Hospital, Boston, Mass (J.K.C.); Department of Radiology, Thomas Jefferson University Hospital, Philadelphia, Pa (A.F.); Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Hospital, Washington, DC (M.G.L.); and Departments of Pediatrics and Radiology, George Washington University School of Medicine, Washington, DC (M.G.L.).
ORCID: 0000-0001-8906-9618

Adam Flanders (A)

Department of Radiology and Biomedical Imaging and Center for Intelligent Imaging, University of California San Francisco, 505 Parnassus Ave, Room M-391, San Francisco, CA 94143 (J.M.); Department of Radiology and MGH and BWH Center for Clinical Data Science, Massachusetts General Hospital, Boston, Mass (J.K.C.); Department of Radiology, Thomas Jefferson University Hospital, Philadelphia, Pa (A.F.); Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Hospital, Washington, DC (M.G.L.); and Departments of Pediatrics and Radiology, George Washington University School of Medicine, Washington, DC (M.G.L.).
ORCID: 0000-0002-4679-0787

Marius George Linguraru (M)

Department of Radiology and Biomedical Imaging and Center for Intelligent Imaging, University of California San Francisco, 505 Parnassus Ave, Room M-391, San Francisco, CA 94143 (J.M.); Department of Radiology and MGH and BWH Center for Clinical Data Science, Massachusetts General Hospital, Boston, Mass (J.K.C.); Department of Radiology, Thomas Jefferson University Hospital, Philadelphia, Pa (A.F.); Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Hospital, Washington, DC (M.G.L.); and Departments of Pediatrics and Radiology, George Washington University School of Medicine, Washington, DC (M.G.L.).

Classifications MeSH