机器学习在胃肠疾病神经影像研究中的应用进展

Application progress on machine learning in the neuroimaging research of gastrointestinal diseases

目前,胃肠疾病发病率居高不下,常见的胃肠病有肠易激综合征、功能性消化不良、炎症性肠病等,严重影响患者的身心健康。基于肠-脑轴的神经机制为寻找与胃肠疾病相关的新型标志物提供了新思路。近年来,基于MRI的胃肠疾病神经机制研究备受关注,同时有研究者尝试利用机器学习方法对其影像数据进行分析和解读,在加深对胃肠疾病神经机制的理解方面取得了不错的效果。本文重点概述了机器学习方法在与胃肠疾病相关的MRI神经影像中的应用情况,旨在为深入探索胃肠疾病的神经机制提供参考,并为提高胃肠疾病的诊断和预后评估效能等方面提供新思路、新手段。

Currently,the prevalence of gastrointestinal diseases remains high,common gastrointestinal diseases include irritable bowel syndrome,functional dyspepsia,and inflammatory bowel disease,etc.,and these diseases seriously affect the physical and mental health of patients.The neural mechanism based on gut-brain axis provides new ideas for finding novel markers related to gastrointestinal diseases.In recent years,the research on the neural mechanism of gastrointestinal diseases based on MRI has attracted much attention.At the same time,some researchers have tried to use machine learning methods to analyze and interpret their image data,which has achieved favorable effects in deepening the understanding of the neural mechanism of gastrointestinal diseases.In this paper,the application status of machine learning methods in MRI neuroimaging related to gastrointestinal diseases is reviewed,aiming at providing references for deeply exploring neural mechanism of gastrointestinal diseases,and providing new ideas and novel approaches for improving diagnosis and prognostic evaluation efficiency of gastrointestinal diseases,etc.