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Articles Published Processes
2/26/2025 4:37:20 AM | Browse: 15 | Download: 35
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Received |
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2024-11-21 12:17 |
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Peer-Review Started |
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2024-11-21 12:19 |
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To Make the First Decision |
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Return for Revision |
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2024-12-13 08:44 |
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Revised |
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2024-12-27 01:00 |
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Second Decision |
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2025-01-07 02:36 |
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Accepted by Journal Editor-in-Chief |
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Accepted by Executive Editor-in-Chief |
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2025-01-08 00:15 |
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Articles in Press |
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2025-01-08 00:15 |
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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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2025-01-10 04:54 |
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Publish the Manuscript Online |
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2025-02-26 04:37 |
| ISSN |
2220-3206 (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) 2025. 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 |
Neuroimaging |
| Manuscript Type |
Editorial |
| Article Title |
Advancing the diagnosis of major depressive disorder: Integrating neuroimaging and machine learning
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| Manuscript Source |
Invited Manuscript |
| All Author List |
Shi-Qi Yin and Ying-Huan Li |
| ORCID |
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| Funding Agency and Grant Number |
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| Corresponding Author |
Ying-Huan Li, Associate Professor, PhD, School of Pharmaceutical Sciences, Capital Medical University, No 10 Xitoutiao, You'anmen Outer, Fengtai District, Beijing 100069, China. yinghuan_li@ccmu.edu.cn |
| Key Words |
Major depressive disorder; Biomarkers; Neuroimaging; Machine learning; Personalized treatment; Resting-state functional magnetic resonance imaging; Functional connectivity; Model accuracy; Major depressive disorder diagnosis |
| Core Tip |
Major depressive disorder (MDD), especially in adolescents, poses considerable diagnostic and therapeutic challenges owing to its heterogeneity and the subjective nature of traditional assessment methods. Recent advances in neuroimaging, combined with machine learning (ML) technologies, have led to the development of promising biomarkers and diagnostic tools for MDD. However, these challenges can be addressed through improved data privacy protection measures, advanced encryption and anonymization techniques, greater model transparency, stricter data quality control, and the establishment of clear ethical and legal frameworks. Such efforts are crucial to ensuring the safe, reliable, and compliant application of ML technologies in MDD diagnosis. |
| Publish Date |
2025-02-26 04:37 |
| Citation |
<p>Yin SQ, Li YH. Advancing the diagnosis of major depressive disorder: Integrating neuroimaging and machine learning. <i>World J Psychiatry</i> 2025; 15(3): 103321</p> |
| URL |
https://www.wjgnet.com/2220-3206/full/v15/i3/103321.htm |
| DOI |
https://dx.doi.org/10.5498/wjp.v15.i3.103321 |
© 2004-2025 Baishideng Publishing Group Inc. All rights reserved. 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
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