Yin SQ, Li YH. Advancing the diagnosis of major depressive disorder: Integrating neuroimaging and machine learning. World J Psychiatry 2025; 15(3): 103321 [DOI: 10.5498/wjp.v15.i3.103321]
Corresponding Author of This Article
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
Research Domain of This Article
Neuroimaging
Article-Type of This Article
Editorial
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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: http://creativecommons.org/licenses/by-nc/4.0/
World J Psychiatry. Mar 19, 2025; 15(3): 103321 Published online Mar 19, 2025. doi: 10.5498/wjp.v15.i3.103321
Advancing the diagnosis of major depressive disorder: Integrating neuroimaging and machine learning
Shi-Qi Yin, Ying-Huan Li
Shi-Qi Yin, Ying-Huan Li, School of Pharmaceutical Sciences, Capital Medical University, Beijing 100069, China
Author contributions: Yin SQ contributed to the writing of original draft; Li YH contributed to writing, review and editing of the manuscript; Yin SQ and Li YH designed the overall concept and outline of the manuscript; all of the authors read and approved the final version of the manuscript to be published.
Conflict-of-interest statement: The authors declare no conflicts of interest.
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/
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
Received: November 21, 2024 Revised: December 27, 2024 Accepted: January 8, 2025 Published online: March 19, 2025 Processing time: 102 Days and 19 Hours
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.
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.
