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

作　　者：Shi-Qi Yin Ying-Huan Li 

机构地区：[1]School of Pharmaceutical Sciences,Capital Medical University,Beijing 100069,China

出　　处：《World Journal of Psychiatry》2025年第3期23-31,共9页世界精神病学杂志(英文)

摘　　要：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.

关 键 词：Major depressive disorder Biomarkers NEUROIMAGING Machine learning Personalized treatment Resting-state functional magnetic resonance imaging Functional connectivity Model accuracy Major depressive disorder diagnosis 

分 类 号：R74[医药卫生—神经病学与精神病学]