摘要阿尔茨海默病(AD)是一种严重影响老年人健康的神经退行性疾病，其早期诊断对延缓病情进展至关重要。本文聚焦多模态数字化技术，系统综述了数字化技术在AD早期识别中的整合应用，涵盖认知评估、影像分析、生物标志物检测及多基因风险预测等领域，旨在提高诊断的准确性和效率。研究发现，基于人工智能的数字化工具通过捕捉行为特征和生理数据，显著提高了筛查效率；机器学习算法结合多模态影像数据提高了脑结构异常的识别灵敏度；数字化生物标志物联合分析实现了AD病理分期的高精度预测。数字化技术在实现多模态标志物联合检测以及优化诊断流程方面都有重要进展。整合多种数字化手段和技术，可以实现AD的早期筛查和诊断，为患者提供更多便捷、高效的选择。

abstractsAlzheimer's disease (AD) is a progressive neurodegenerative disorder that seriously affects the health of the elderly, and the early diagnosis is crucial to slow down the progression of the disease. This review systematically examines the integrative applications of multimodal digital technologies in early AD identification, encompassing cognitive assessment, neuroimaging analysis, biomarker detection, and polygenic risk prediction, with the goal of enhancing diagnostic accuracy and operational efficiency. It was found that artificial intelligence-driven digital tools significantly improved screening efficiency by capturing subtle behavioral patterns and physiological signatures. Machine learning algorithms integrated with multimodal neuroimaging data optimize sensitivity in detecting structural brain abnormalities, while combinatorial analysis of digital biomarkers enables high-precision staging of AD pathology. Recent advancements highlight the critical role of digital technologies in facilitating multimodal biomarker integration and streamlining diagnostic workflows. The convergence of these innovative approaches provides a robust framework for early AD screening, offering patients accessible and efficient diagnostic pathways.