老年人认知障碍检测与管理的技术进展：趋势、挑战与未来方向
Technological Advances in Detecting and Managing Cognitive Impairment in Older Adults: Trends, Challenges, and Future Directions
浏览论文内容
中文总结 AI 辅助
本文综合老年人认知障碍检测与管理的技术进展，提出跨学科分类法等成果，指出现有模型多依赖小数据集，展望了可信多模态纵向验证系统的发展前景。
中文摘要 AI 辅助
随着人口老龄化，从轻度认知障碍（MCI）到痴呆的认知衰退将成为未来几十年的标志性健康挑战，但常规评估往往会错过其最早的迹象。本文批判性地综合了老年人认知障碍检测与管理的最新技术进展，涵盖神经生理信号（主要为脑电图，EEG）、结构与分子神经成像（MRI及淀粉样蛋白/ tau蛋白PET）、血液生物标志物以及数字标志物，这些技术通过人工智能（AI）、机器学习（ML）和深度学习（DL）实现整合。除了综述，本文还提供了跨学科分类法、强调受试者与位点独立验证的方法学严谨性视角、将分层筛查与干预相联系的整合早期检测框架，以及检测方法、干预措施和风险与保护因素的比较表。脑电图标志物（α/θ波变化、P300潜伏期）和深度模型（卷积神经网络（CNNs）、长短期记忆网络（LSTM）/双向长短期记忆网络（BiLSTM）、Transformer、自监督脑电图基础模型）报告了较高的准确率，但许多模型基于小型、单中心数据集，难以通过严格的外部验证。在其他方面，进展切实可见：血浆p-tau217已达到临床应用水平，首款血液检测将于2025年获批用于辅助阿尔茨海默病诊断；抗淀粉样蛋白疗法（仑卡奈单抗（lecanemab）、多纳单抗（donanemab））尽管益处有限且存在争议仍获批准；多域生活方式预防已成熟。可穿戴、远程、语音及虚拟现实工具支持连续、生态有效的监测，多模态融合提高了灵敏度和特异性。仍存在标准化、可解释性、数据隐私及公平、经外部验证的部署等障碍。该领域近期的前景在于将早期检测与可操作的个性化护理相联系的、可信的、多模态的、经纵向验证的系统。
英文摘要
As populations age, cognitive decline from mild cognitive impairment (MCI) to dementia is a defining health challenge of the coming decades, yet routine assessment often misses its earliest signs. This article critically synthesizes recent technological advances for detecting and managing cognitive impairment in older adults, spanning neurophysiological signals (chiefly electroencephalography, EEG), structural and molecular neuroimaging (MRI and amyloid/tau PET), blood-based biomarkers, and digital markers, integrated through artificial intelligence (AI), machine learning (ML), and deep learning (DL). Beyond summarizing, it contributes a cross-disciplinary taxonomy, a methodological-rigor lens foregrounding subject- and site-independent validation, an integrative early-detection framework linking tiered screening to intervention, and comparison tables of detection methods, interventions, and risk and protective factors. EEG markers (alpha/theta changes, P300 latency) and deep models (CNNs, LSTM/BiLSTM, transformers, self-supervised EEG foundation models) report strong accuracy, yet many rest on small, single-site datasets unlikely to survive rigorous external validation. Elsewhere, gains are tangible: plasma p-tau217 has reached clinical utility, with the first blood test cleared to aid Alzheimer's diagnosis in 2025; anti-amyloid therapies (lecanemab, donanemab) are approved despite modest, contested benefits; and multidomain lifestyle prevention has matured. Wearable, remote, speech, and virtual-reality tools enable continuous, ecologically valid monitoring, and multimodal fusion improves sensitivity and specificity. Barriers remain: standardization, explainability, data privacy, and equitable, externally validated deployment. The field's near-term promise lies in trustworthy, multimodal, longitudinally validated systems linking early detection to actionable, personalized care.
发表机构
- Indian Institute of Technology Bombay（印度理工学院孟买分校）
- T-Systems ICT India Pvt. Ltd.（德国电信系统印度信息通信技术私人有限公司）
机构由 AI 辅助整理，请以论文原文为准。
