StrokeGuard：用于院前卒中评估的多智能体引导系统
StrokeGuard: A Multi-Agent Guided System for Prehospital Stroke Assessment
- Institute of Medical Robotics（医学机器人研究所）
- School of Biomedical Engineering（生物医学工程学院）
- Shanghai Jiao Tong University（上海交通大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
StrokeGuard是一种多智能体引导系统，采用双通道智能体机制，在模拟院前场景中使MATES-9总分较纸质FAST式表格提升23.8%，优化了院前卒中评估的标准化与可执行性。
AI中文摘要：
院前卒中评估旨在极窄的时间窗口内通过标准化流程准确识别卒中症状并做出快速决策，从而为后续治疗节省宝贵时间。临床实践中，基于FAST的量表被广泛用于院前卒中评估，其通过发布指令指导受试者完成特定动作，以筛查面部、手臂和言语功能。但在家庭和社区环境中，非专业用户常遇到描述不准确、症状观察不完整、操作流程复杂等挑战，可能导致评估结果不准确或有偏差。为应对这些挑战，本文提出StrokeGuard：一种专为院前卒中评估设计的多智能体引导系统，旨在让移动FAST筛查更具标准化和可执行性。具体而言，为克服传统单智能体系统在流程容错性和用户引导能力方面的局限，StrokeGuard采用双通道智能体机制，将正式评估（即面瘫、手臂无力、言语障碍）与流程支持（如步骤提示、错误纠正和实时反馈）相分离。该系统通过多智能体协作、双通道交互、状态机控制及阶段局部 fallback 恢复机制引导评估流程；阶段特定评分由受限预训练视频评估模块负责，同时将证据源记录与结构化报告生成相结合。用户评估采用MATES-9，这是一种用于测量多步骤AI引导任务中用户体验的探索性量表。在模拟院前场景中，StrokeGuard使MATES-9总分较纸质FAST式表格提高了10.83分，对应23.8%的相对提升。
英文摘要：
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.
