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6/4/2024 3:58:50 PM | Browse: 49 | Download: 59
Publication Name World Journal of Clinical Cases
Manuscript ID 93409
Country Qatar
Received
2024-02-26 17:35
Peer-Review Started
2024-02-26 17:35
To Make the First Decision
Return for Revision
2024-04-04 03:45
Revised
2024-04-04 23:11
Second Decision
2024-04-18 02:37
Accepted by Journal Editor-in-Chief
Accepted by Company Editor-in-Chief
2024-04-18 07:47
Articles in Press
2024-04-18 07:47
Publication Fee Transferred
Edit the Manuscript by Language Editor
Typeset the Manuscript
2024-05-27 15:44
Publish the Manuscript Online
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
Permissions For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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
Manuscript Source Invited Manuscript
All Author List Jaber H Jaradat and Abdulqadir J Nashwan
Funding Agency and Grant Number
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
Full Article (PDF) WJCC-12-2921-with-cover.pdf
Full Article (Word) WJCC-12-2921.docx
Manuscript File 93409_Auto_Edited-YJP.docx
Answering Reviewers 93409-answering-reviewers.pdf
Audio Core Tip 93409-audio.mp3
Conflict-of-Interest Disclosure Form 93409-conflict-of-interest-statement.pdf
Copyright License Agreement 93409-copyright-assignment.pdf
Non-Native Speakers of English Editing Certificate 93409-non-native-speakers.pdf
Peer-review Report 93409-peer-reviews.pdf
Scientific Misconduct Check 93409-scientific-misconduct-check.png
Scientific Editor Work List 93409-scientific-editor-work-list.pdf
CrossCheck Report 93409-crosscheck-report.pdf