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6/4/2024 3:58:50 PM | Browse: 208 | Download: 311
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Received |
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2024-02-26 17:35 |
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Peer-Review Started |
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2024-02-26 17:35 |
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To Make the First Decision |
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Return for Revision |
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2024-04-04 03:45 |
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Revised |
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2024-04-04 23:11 |
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Second Decision |
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2024-04-18 02:37 |
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Accepted by Journal Editor-in-Chief |
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Accepted by Executive Editor-in-Chief |
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2024-04-18 07:47 |
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Articles in Press |
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2024-04-18 07:47 |
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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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2024-05-27 15:44 |
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Publish the Manuscript Online |
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2024-06-04 15:55 |
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) 2024. 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 |
Methodology |
Manuscript Type |
Editorial |
Article Title |
Revolutionizing disease diagnosis and management: Open-access magnetic resonance imaging datasets a challenge for artificial intelligence driven liver iron quantification
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Manuscript Source |
Invited Manuscript |
All Author List |
Jaber H Jaradat and Abdulqadir J Nashwan |
Funding Agency and Grant Number |
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Corresponding Author |
Abdulqadir J Nashwan, MSc, Research Scientist, Department of Nursing , Hamad Medical Corporation, Rayyan Road, Doha 3050, Qatar. anashwan@hamad.qa |
Key Words |
Liver diseases; Magnetic resonance imaging; Iron quantification; Machine learning; Deep learning |
Core Tip |
This editorial emphasizes the revolutionary impact of artificial intelligence (AI), particularly machine learning and deep learning techniques like convolutional neural networks (CNNs), in healthcare. Highlighting the limitations of traditional, slightly invasive blood tests for assessing body iron load, it advocates for magnetic resonance imaging 's non-invasive advantages. The editorial underscores the critical role of AI and CNNs in improving disease diagnosis and treatment, especially through the precise detection of minor changes in liver iron levels. However, it points out the significant hurdle of lacking open-access datasets, which hampers medical research progress. The call for standardized datasets is a crucial step towards leveraging AI in medical imaging, promising to transform patient care and management. |
Publish Date |
2024-06-04 15:55 |
Citation |
<p>Jaradat JH, Nashwan AJ. Revolutionizing disease diagnosis and management: Open-access magnetic resonance imaging datasets a challenge for artificial intelligence driven liver iron quantification. <i>World J Clin Cases</i> 2024; 12(17): 2921-2924</p> |
URL |
https://www.wjgnet.com/2307-8960/full/v12/i17/2921.htm |
DOI |
https://dx.doi.org/10.12998/wjcc.v12.i17.2921 |
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