BRAINS：面向阿尔茨海默病检测与监测的检索增强系统

BRAINS: A Retrieval-Augmented System for Alzheimer's Detection and Monitoring

November 4, 2025

作者: Rajan Das Gupta, Md Kishor Morol, Nafiz Fahad, Md Tanzib Hosain, Sumaya Binte Zilani Choya, Md Jakir Hossen

cs.AI

摘要

随着阿尔茨海默病（AD）全球负担持续加重，早期精准检测变得尤为关键——在先进诊断工具稀缺地区更是如此。为此，我们提出BRAINS（基于生物医学检索增强智能的神经退行性疾病筛查系统）应对这一挑战。该创新系统利用大语言模型（LLMs）强大的推理能力实现阿尔茨海默病的检测与监测。 BRAINS采用双模块架构：认知诊断模块与病例检索模块。诊断模块运用经认知评估和神经影像数据集（包括MMSE量表、CDR评分及脑容量指标）微调的LLMs，对阿尔茨海默病风险进行结构化评估。病例检索模块则将患者档案编码为潜在表征，并从精编知识库中检索相似病例。这些辅助病例通过病例融合层与输入档案进行整合，以增强上下文理解。最终，融合后的表征结合临床提示词进行推理分析。 真实世界数据集上的评估表明，BRAINS在疾病严重程度分类和认知衰退早期迹象识别方面成效显著。该系统不仅展现出作为可扩展、可解释的早期阿尔茨海默病检测辅助工具的巨大潜力，更为该领域的未来应用带来希望。

English

As the global burden of Alzheimer's disease (AD) continues to grow, early and accurate detection has become increasingly critical, especially in regions with limited access to advanced diagnostic tools. We propose BRAINS (Biomedical Retrieval-Augmented Intelligence for Neurodegeneration Screening) to address this challenge. This novel system harnesses the powerful reasoning capabilities of Large Language Models (LLMs) for Alzheimer's detection and monitoring. BRAINS features a dual-module architecture: a cognitive diagnostic module and a case-retrieval module. The Diagnostic Module utilizes LLMs fine-tuned on cognitive and neuroimaging datasets -- including MMSE, CDR scores, and brain volume metrics -- to perform structured assessments of Alzheimer's risk. Meanwhile, the Case Retrieval Module encodes patient profiles into latent representations and retrieves similar cases from a curated knowledge base. These auxiliary cases are fused with the input profile via a Case Fusion Layer to enhance contextual understanding. The combined representation is then processed with clinical prompts for inference. Evaluations on real-world datasets demonstrate BRAINS effectiveness in classifying disease severity and identifying early signs of cognitive decline. This system not only shows strong potential as an assistive tool for scalable, explainable, and early-stage Alzheimer's disease detection, but also offers hope for future applications in the field.