基于深度学习的阿尔茨海默病结构影像组学研究进展
Research Progress on Structural Radiomics of Alzheimer’s Disease Based on Deep Learning
摘要: 阿尔茨海默病(AD)作为全球范围内最常见的神经退行性疾病之一，其早期诊断和治疗对改善患者生活质量至关重要。近年来，深度学习(DL)技术的快速发展为AD的结构影像组学研究提供了新的机遇。本文通过系统检索PubMed、Web of Science、中国知网(CNKI)等数据库中2018~2024年发表的相关文献，以“深度学习”“阿尔茨海默病”“结构磁共振成像”“影像组学”为核心关键词，筛选出高质量研究文献82篇，其中英文文献68篇、中文文献14篇，重点分析基于深度学习技术在AD结构影像组学领域的多模态脑连接组信息融合、早期认知障碍检测及多阶段AD诊断的最新技术和成果。同时，系统梳理不同深度学习架构在AD风险预测、轻度认知障碍(MCI)识别和AD患者分类中的应用，指出当前研究面临的挑战并提出未来发展方向。旨在为相关领域研究者提供理论支持和技术参考，推动基于结构影像组学的AD智能诊断技术发展。
Abstract: Alzheimer’s disease (AD) is one of the most common neurodegenerative diseases worldwide, and its early diagnosis and treatment are crucial for improving patients’ quality of life. In recent years, the rapid development of deep learning (DL) technology has provided new opportunities for structural radiomics research of AD. In this study, relevant literatures published from 2018 to 2024 were systematically searched in databases such as PubMed, Web of Science, and China National Knowledge Infrastructure (CNKI), with “deep learning”, “Alzheimer’s disease”, “structural magnetic resonance imaging”, and “radiomics” as core keywords. A total of 82 high-quality research literatures were selected, including 68 English literatures and 14 Chinese literatures. The latest technologies and achievements of DL technology in multi-modal brain connectome information fusion, early cognitive impairment detection, and multi-stage AD diagnosis in the field of AD structural radiomics were focused on analyzing. At the same time, the application of different DL architectures in AD risk prediction, mild cognitive impairment (MCI) identification, and AD patient classification was systematically reviewed, the challenges faced in current research were pointed out, and future development directions were proposed. This study aims to provide theoretical support and technical reference for researchers in related fields, and promote the development of AD intelligent diagnosis technology based on structural radiomics.
文章引用：高希春, 刘国标, 黎浩良, 黄慈花, 许剑涛, 黄伟刚, 陈弘, 张建波, 谭志坚, 成程. 基于深度学习的阿尔茨海默病结构影像组学研究进展[J]. 临床医学进展, 2025, 15(10): 2317-2325. https://doi.org/10.12677/acm.2025.15103015

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