StrokeGuard用多智能体系统让非专业人员也能准确完成院前中风筛查。
StrokeGuard: A Multi-Agent Guided System for Prehospital Stroke Assessment
- 采用双通道智能体分工，分别负责评估与操作指导，提升容错与引导能力。
- 在模拟场景中用户评分提升23.8%，较传统纸质FAST表显著改善体验。
- 适合家庭和社区场景，帮助非医护人员高效完成中风早期识别。
院前中风评估旨在极短时间内通过标准化流程准确识别中风症状并作出快速决策，以争取后续治疗的宝贵时间。临床实践中，基于FAST的筛查工具通过指导受试者执行特定动作来检测面部、肢体和语言功能异常。然而，在家庭和社区环境中，非专业用户常面临描述不准确、症状观察不全、操作复杂等问题，导致评估结果偏差。为此，本文提出StrokeGuard：一种面向院前中风评估的多智能体引导系统，使移动FAST筛查更标准化、可执行。该系统采用双通道机制，将正式评估（如面瘫、肢无力、言语障碍）与流程支持（如步骤提示、错误纠正、实时反馈）分离，通过多智能体协作、双通道交互、状态机控制及阶段局部回滚机制引导评估流程。各阶段评分由受限预训练视频分析模块完成，证据记录与结构化报告生成融合。用户评估使用MATES-9量表，结果显示在模拟院前场景中，StrokeGuard相比纸质FAST表总分提升10.83分，相对提高23.8%。
原文摘要 · Abstract (English)
Prehospital stroke assessment aims to accurately identify stroke symptoms and make rapid decisions through standardized procedures within an extremely narrow time window, thereby saving valuable time for subsequent treatment. In clinical practice, FAST-based scales are widely used for prehospital stroke assessment by issuing instructions that guide subjects to perform specific actions to screen facial, arm, and speech functions. However, in home and community settings, non-clinical users often encounter challenges such as inaccurate descriptions, incomplete symptom observation, and difficult operational procedures, which may lead to inaccurate or biased assessment results. To address these challenges, this paper presents StrokeGuard: a multi-agent guided system designed for prehospital stroke assessment that makes mobile FAST screening more standardized and executable. Specifically, to overcome the limitations of traditional single-agent systems in terms of procedural fault tolerance and user guidance capability, StrokeGuard adopts a dual-channel agent mechanism that separates formal assessment (i.e., facial palsy, arm weakness, speech impairment) from procedural support (e.g., step prompts, error correction, and real-time feedback). It guides the assessment process through multi-agent collaboration, dual-channel interaction, state-machine control, and stage-local fallback recovery mechanisms. Stage-specific scoring is delegated to constrained pretrained video assessment modules, while evidence source records are integrated with structured report generation. The user evaluation uses MATES-9, an exploratory scale for measuring user experience in multistep AI-guided tasks. In a simulated prehospital scenario, StrokeGuard improves the MATES-9 total score over a paper FAST-style form by 10.83 points, corresponding to a 23.8% relative increase.
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