Advancing the diagnosis of major depressive disorder: Integrating neuroimaging and machine learning.

IF 3.9 4区 医学 Q1 PSYCHIATRY
Shi-Qi Yin, Ying-Huan Li
{"title":"Advancing the diagnosis of major depressive disorder: Integrating neuroimaging and machine learning.","authors":"Shi-Qi Yin, Ying-Huan Li","doi":"10.5498/wjp.v15.i3.103321","DOIUrl":null,"url":null,"abstract":"<p><p>Major depressive disorder (MDD), a psychiatric disorder characterized by functional brain deficits, poses considerable diagnostic and treatment challenges, especially in adolescents owing to varying clinical presentations. Biomarkers hold substantial clinical potential in the field of mental health, enabling objective assessments of physiological and pathological states, facilitating early diagnosis, and enhancing clinical decision-making and patient outcomes. Recent breakthroughs combine neuroimaging with machine learning (ML) to distinguish brain activity patterns between MDD patients and healthy controls, paving the way for diagnostic support and personalized treatment. However, the accuracy of the results depends on the selection of neuroimaging features and algorithms. Ensuring privacy protection, ML model accuracy, and fostering trust are essential steps prior to clinical implementation. Future research should prioritize the establishment of comprehensive legal frameworks and regulatory mechanisms for using ML in MDD diagnosis while safeguarding patient privacy and rights. By doing so, we can advance accuracy and personalized care for MDD.</p>","PeriodicalId":23896,"journal":{"name":"World Journal of Psychiatry","volume":"15 3","pages":"103321"},"PeriodicalIF":3.9000,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11886342/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"World Journal of Psychiatry","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.5498/wjp.v15.i3.103321","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PSYCHIATRY","Score":null,"Total":0}
引用次数: 0

Abstract

Major depressive disorder (MDD), a psychiatric disorder characterized by functional brain deficits, poses considerable diagnostic and treatment challenges, especially in adolescents owing to varying clinical presentations. Biomarkers hold substantial clinical potential in the field of mental health, enabling objective assessments of physiological and pathological states, facilitating early diagnosis, and enhancing clinical decision-making and patient outcomes. Recent breakthroughs combine neuroimaging with machine learning (ML) to distinguish brain activity patterns between MDD patients and healthy controls, paving the way for diagnostic support and personalized treatment. However, the accuracy of the results depends on the selection of neuroimaging features and algorithms. Ensuring privacy protection, ML model accuracy, and fostering trust are essential steps prior to clinical implementation. Future research should prioritize the establishment of comprehensive legal frameworks and regulatory mechanisms for using ML in MDD diagnosis while safeguarding patient privacy and rights. By doing so, we can advance accuracy and personalized care for MDD.

推进重度抑郁症的诊断:整合神经影像学和机器学习。
重度抑郁症(MDD)是一种以功能性脑缺陷为特征的精神障碍,由于临床表现各异,对诊断和治疗构成了相当大的挑战,特别是在青少年中。生物标志物在心理健康领域具有巨大的临床潜力,能够客观评估生理和病理状态,促进早期诊断,增强临床决策和患者预后。最近的突破将神经成像与机器学习(ML)相结合,以区分重度抑郁症患者和健康对照者之间的大脑活动模式,为诊断支持和个性化治疗铺平了道路。然而,结果的准确性取决于神经影像学特征和算法的选择。确保隐私保护、ML模型准确性和培养信任是临床实施之前的必要步骤。未来的研究应优先考虑在保护患者隐私和权利的同时,建立全面的法律框架和监管机制,以便在MDD诊断中使用ML。通过这样做,我们可以提高对重度抑郁症的准确性和个性化护理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
6.50%
发文量
110
期刊介绍: The World Journal of Psychiatry (WJP) is a high-quality, peer reviewed, open-access journal. The primary task of WJP is to rapidly publish high-quality original articles, reviews, editorials, and case reports in the field of psychiatry. In order to promote productive academic communication, the peer review process for the WJP is transparent; to this end, all published manuscripts are accompanied by the anonymized reviewers’ comments as well as the authors’ responses. The primary aims of the WJP are to improve diagnostic, therapeutic and preventive modalities and the skills of clinicians and to guide clinical practice in psychiatry.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信