基于多模态MRI和人工智能预测轻度认知障碍向阿尔茨海默病转化的研究进展。

IF 2.8 3区 医学 Q2 CLINICAL NEUROLOGY
Frontiers in Neurology Pub Date : 2025-06-02 eCollection Date: 2025-01-01 DOI:10.3389/fneur.2025.1596632
Min Ai, Yu Liu, Dan Liu, Chengxi Yan, Xia Wang, Xun Chen
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引用次数: 0

摘要

预测轻度认知障碍(mild cognitive impairment, MCI)向阿尔茨海默病(Alzheimer's disease, AD)的转变对痴呆症的预防和改善患者预后具有重要的临床意义。多模态磁共振成像(MRI)技术(包括结构MRI、功能MRI和脑灌注MRI)可以从多个维度获得大脑的形态、结构和功能信息,为揭示MCI向AD转化过程中的病理生理机制提供了重要依据。基于深度学习和机器学习的人工智能方法,以其强大的数据处理和模式识别能力,在挖掘多模态MRI数据特征和构建MCI转换预测模型方面显示出巨大的潜力。因此,本文系统回顾了多模态MRI技术在捕获与MCI转换相关的大脑变化方面的研究进展,以及AI算法构建高效预测模型的实践经验,分析了目前研究面临的技术挑战,并探讨了未来发展方向,旨在为早期准确预测MCI转换和制定干预策略提供科学参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research progress in predicting the conversion from mild cognitive impairment to Alzheimer's disease via multimodal MRI and artificial intelligence.

Predicting the transition from mild cognitive impairment (MCI) to Alzheimer's disease (AD) has important clinical significance for dementia prevention and improving patient prognosis. Multimodal magnetic resonance imaging (MRI) techniques (including structural MRI, functional MRI, and cerebral perfusion MRI) can yield information on the morphology, structure, and function of the brain from multiple dimensions, providing a key basis for revealing the pathophysiological mechanisms during the conversion from MCI to AD. Artificial intelligence (AI) methods based on deep learning and machine learning, with their powerful data processing and pattern recognition capabilities, have shown great potential in mining the features of multimodal MRI data and constructing prediction models for MCI conversion. Therefore, this paper systematically reviews the research progress of multimodal MRI techniques in capturing brain changes related to MCI conversion, as well as the practical experience of AI algorithms in constructing efficient prediction models, analyses the current technical challenges faced by the research, and discusses future directions, with the goal of providing a scientific reference for the early and accurate prediction of MCI conversion and the formulation of intervention strategies.

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来源期刊
Frontiers in Neurology
Frontiers in Neurology CLINICAL NEUROLOGYNEUROSCIENCES -NEUROSCIENCES
CiteScore
4.90
自引率
8.80%
发文量
2792
审稿时长
14 weeks
期刊介绍: The section Stroke aims to quickly and accurately publish important experimental, translational and clinical studies, and reviews that contribute to the knowledge of stroke, its causes, manifestations, diagnosis, and management.
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