首个专用于阿尔茨海默病分析的AI代理，能处理多模态数据并协同决策。
ADAgent: LLM Agent for Alzheimer's Disease Analysis with Collaborative Coordinator
- 基于大模型构建协作式诊断代理，支持多模态输入与任务组合
- 多模态诊断准确率提升2.7%，预后预测提升0.7%，影像诊断也显著优化
- 适合临床辅助决策与跨模态研究，可应对缺失数据与复杂任务
阿尔茨海默病（AD）是一种进行性、不可逆的神经退行性疾病。早期精准诊断对及时干预和治疗规划至关重要，以延缓神经退行性进展。然而，现有方法多依赖单一模态数据，与临床专家的多维度评估方式不符。尽管部分深度学习方法可处理多模态数据，但仅限特定任务且输入模态固定，无法适应任意组合。因此亟需一种能应对多样化AD任务、处理多模态或缺失输入，并融合多种先进方法的系统。本文提出ADAgent，首个专用于AD分析的AI代理，基于大语言模型（LLM），可响应用户查询并支持决策。ADAgent集成推理引擎、专业医疗工具与协作结果协调器，实现多模态诊断与预后任务的协同。大量实验表明，ADAgent优于当前最优方法，在多模态诊断中准确率提升2.7%，多模态预后提升0.7%，并在MRI与PET诊断任务中均有显著改进。
原文摘要 · Abstract (English)
Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative disease. Early and precise diagnosis of AD is crucial for timely intervention and treatment planning to alleviate the progressive neurodegeneration. However, most existing methods rely on single-modality data, which contrasts with the multifaceted approach used by medical experts. While some deep learning approaches process multi-modal data, they are limited to specific tasks with a small set of input modalities and cannot handle arbitrary combinations. This highlights the need for a system that can address diverse AD-related tasks, process multi-modal or missing input, and integrate multiple advanced methods for improved performance. In this paper, we propose ADAgent, the first specialized AI agent for AD analysis, built on a large language model (LLM) to address user queries and support decision-making. ADAgent integrates a reasoning engine, specialized medical tools, and a collaborative outcome coordinator to facilitate multi-modal diagnosis and prognosis tasks in AD. Extensive experiments demonstrate that ADAgent outperforms SOTA methods, achieving significant improvements in accuracy, including a 2.7% increase in multi-modal diagnosis, a 0.7% improvement in multi-modal prognosis, and enhancements in MRI and PET diagnosis tasks.
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