Application of Deep Learning to Ischemic and Hemorrhagic Stroke Computed Tomography and Magnetic Resonance Imaging.


Journal

Seminars in ultrasound, CT, and MR
ISSN: 1558-5034
Titre abrégé: Semin Ultrasound CT MR
Pays: United States
ID NLM: 8504689

Informations de publication

Date de publication:
Apr 2022
Historique:
entrez: 27 3 2022
pubmed: 28 3 2022
medline: 1 4 2022
Statut: ppublish

Résumé

Deep Learning (DL) algorithm holds great potential in the field of stroke imaging. It has been applied not only to the "downstream" side such as lesion detection, treatment decision making, and outcome prediction, but also to the "upstream" side for generation and enhancement of stroke imaging. This paper aims to comprehensively overview the common applications of DL to stroke imaging. In the future, more standardized imaging datasets and more extensive studies are needed to establish and validate the role of DL in stroke imaging.

Identifiants

pubmed: 35339255
pii: S0887-2171(22)00016-6
doi: 10.1053/j.sult.2022.02.004
pii:
doi:

Types de publication

Journal Article Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

147-152

Informations de copyright

Copyright © 2022 Elsevier Inc. All rights reserved.

Auteurs

Guangming Zhu (G)

Department of Radiology, Neuroradiology Section, Stanford University School of Medicine, Stanford, CA.

Hui Chen (H)

Department of Radiology, Neuroradiology Section, Stanford University School of Medicine, Stanford, CA.

Bin Jiang (B)

Department of Radiology, Neuroradiology Section, Stanford University School of Medicine, Stanford, CA.

Fei Chen (F)

Department of Neurology, Xuan Wu hospital, Capital Meidcal University, Beijing, China.

Yuan Xie (Y)

Subtle Medical Inc, Menlo Park, CA.

Max Wintermark (M)

Department of Radiology, Neuroradiology Section, Stanford University School of Medicine, Stanford, CA. Electronic address: max.wintermark@gmail.com.

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Classifications MeSH