Ai Min, Liu Yu, Liu Dan, Yan Chengxi, Wang Xia, Chen Xun
Department of Anesthesiology, Nanan District People's Hospital of Chongqing, Chongqing, China.
Department of Radiology, Chongqing Public Health Medical Center, Chongqing, China.
Front Neurol. 2025 Jun 2;16:1596632. doi: 10.3389/fneur.2025.1596632. eCollection 2025.
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.
预测从轻度认知障碍（MCI）向阿尔茨海默病（AD）的转变对于痴呆症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）可以从多个维度获取有关大脑形态、结构和功能的信息，为揭示从MCI转变为AD过程中的病理生理机制提供关键依据。基于深度学习和机器学习的人工智能（AI）方法具有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转变预测模型方面显示出巨大潜力。因此，本文系统综述了多模态MRI技术在捕捉与MCI转变相关的大脑变化方面的研究进展，以及AI算法在构建高效预测模型方面的实践经验，分析了该研究目前面临的技术挑战，并探讨了未来方向，旨在为MCI转变的早期准确预测和干预策略的制定提供科学参考。
