Advances of multiparametric MRI and machine learning in cognitive impairment related to cerebral small vessel disease
黄晶 1罗天友1
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作者信息
1. 重庆医科大学附属第一医院放射科,重庆 400016
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摘要
随着人口老龄化加剧,与年龄相关的脑小血管病(cerebral small vessel disease,CSVD)发病率不断升高.CSVD通常导致患者认知功能障碍,甚至痴呆,已成为亟待解决的重要公共卫生问题.但目前CSVD相关认知功能障碍的发病机制尚未完全阐明,且缺乏早期诊断和治疗的有效手段.随着神经影像学技术和人工智能的飞速发展,多参数MRI和机器学习在CSVD相关认知功能障碍的辅助诊断和发病机制探索中发挥着越来越重要的作用.本文就近年来相关研究进展作一综述,旨在为阐明CSVD相关认知功能障碍的神经机制及其早期诊断提供全面、客观的影像学依据.
Abstract
With the aging of the population,the prevalence of age-related cerebral small vessel disease(CSVD)is on the rise.CSVD frequently results in cognitive impairment and dementia,making it a pressing public health concern.Nevertheless,the pathogenesis of cognitive impairment related to CSVD has not yet been fully elucidated,and there is a lack of effective methods for early diagnosis and treatment.With the rapid development of neuroimaging technology and artificial intelligence,multiparametric MRI and machine learning are playing an increasingly important role in the auxiliary diagnosis and pathogenesis exploration of cognitive impairment related to CSVD.This article provides a review of the relevant research progress in recent years,aiming to provide comprehensive and objective imaging evidence for elucidating the neural mechanisms and early diagnosis of cognitive impairment related to CSVD.
关键词
脑小血管病/认知功能障碍/磁共振成像/多参数磁共振成像/机器学习
Key words
cerebral small vessel disease/cognitive impairment/magnetic resonance imaging/multiparametric magnetic resonance imaging/machine learning
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