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Articles Published Processes
6/2/2023 11:04:53 AM | Browse: 193 | Download: 483
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Received |
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2023-01-04 02:23 |
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Peer-Review Started |
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2023-01-04 02:25 |
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To Make the First Decision |
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Return for Revision |
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2023-01-20 00:55 |
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Revised |
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2023-01-30 02:11 |
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Second Decision |
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2023-05-06 02:38 |
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Accepted by Journal Editor-in-Chief |
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Accepted by Executive Editor-in-Chief |
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2023-05-06 06:48 |
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Articles in Press |
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2023-05-06 06:48 |
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Publication Fee Transferred |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2023-05-19 06:08 |
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Publish the Manuscript Online |
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2023-06-02 11:04 |
| ISSN |
2307-8960 (online) |
| Open Access |
This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/ |
| Copyright |
© The Author(s) 2023. Published by Baishideng Publishing Group Inc. All rights reserved. |
| Article Reprints |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| Category |
Radiology, Nuclear Medicine & Medical Imaging |
| Manuscript Type |
Minireviews |
| Article Title |
Review of deep learning and artificial intelligence models in fetal brain magnetic resonance imaging
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| Manuscript Source |
Invited Manuscript |
| All Author List |
Farzan Vahedifard, Jubril O Adepoju, Mark Supanich, Hua Asher Ai, Xuchu Liu, Mehmet Kocak, Kranthi K Marathu and Sharon E Byrd |
| ORCID |
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| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| Colonel Robert R McCormick Professorship of Diagnostic Imaging Fund at Rush University Medical Center (The Activity Number is 1233-161-84) |
8410152-03 |
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| Corresponding Author |
Farzan Vahedifard, MD, Research Fellow, Department of Diagnostic Radiology and Nuclear Medicine, Rush Medical College, 1620 W Harrison St, Jelke Building, Unit 169, Chicago, IL 606012, United States. farzan_vahedifard@rush.edu |
| Key Words |
Artificial intelligence; Fetal brain; Magnetic resonance imaging; Neuroimaging |
| Core Tip |
The manual detection and segmentation of fetal brain magnetic resonance imaging (MRI) may be time-consuming, and susceptible to interpreter experience. During the past decade, artificial intelligence (AI) algorithms, particularly deep learning, have made impressive progress in image recognition tasks. A machine learning approach may help detect these problems early and improve the diagnosis and follow-up process. This narrative review paper investigates the role of AI and machine learning methods in fetal brain MRI. |
| Publish Date |
2023-06-02 11:04 |
| Citation |
Vahedifard F, Adepoju JO, Supanich M, Ai HA, Liu X, Kocak M, Marathu KK, Byrd SE. Review of deep learning and artificial intelligence models in fetal brain magnetic resonance imaging. World J Clin Cases 2023; 11(16): 3725-3735 |
| URL |
https://www.wjgnet.com/2307-8960/full/v11/i16/3725.htm |
| DOI |
https://dx.doi.org/10.12998/wjcc.v11.i16.3725 |
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