摘要
精神分裂症(SZ)是一种严重精神疾病,采用传统方法进行诊断易漏、误诊。利用机器学习(ML)算法可从功能MRI(fMRI)数据中提取SZ相关特征,并进行诊断及疗效预测。本文就基于fMRI的ML用于诊断和治疗SZ的研究进展进行综述。
Schizophrenia(SZ)is a serious mental illness,and traditional diagnostic methods are prone to missed and misdiagnosis.Using machine learning(ML)algorithms can extract SZ relative features from functional MRI(fMRI)data,hence being helpful for diagnosing and predicting treatment response of SZ.The research progresses of ML based on fMRI for diagnosis and treatment of SZ were reviewed in this article.
作者
刘晨宇
周素妙
易芸
黄园园
李荷花
冯仕轩
黎浚豪
吴逢春
LIU Chenyu;ZHOU Sumiao;YI Yun;HUANG Yuanyuan;LI Hehua;FENG Shixuan;LI Junhao;WU Fengchun(Department of Psychiatry,The Affiliated Brain Hospital of Guangzhou Medical University,Guangzhou 510370,China;Department of Psychiatry,The Guangxi Zhuang Autonomous Region Brain Hospital,Liuzhou 545000,China;Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders,Guangzhou 510370,China;Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province,Guangzhou 510370,China)
出处
《中国医学影像技术》
CSCD
北大核心
2023年第12期1898-1901,共4页
Chinese Journal of Medical Imaging Technology
基金
国家自然科学基金(82301688)
广州市科技计划项目(202201010093)
广州市卫生健康科技项目(20231A010036)。
关键词
精神分裂症
机器学习
磁共振成像
schizophrenia
machine learning
magnetic resonance imaging
