发表机构
Cleveland Clinic; Kent State University（克利夫兰诊所; 肯特州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
OA-MAP是用于可解释膝骨关节炎进展评估的自主多智能体多模态框架，含模态专用智能体与协调智能体，经600名参与者数据验证，在100人测试集上结构进展AUROC达0.80、疼痛进展达0.68，支持交互式审查。
AI 中文摘要
膝骨关节炎（KOA）进展预测可支持患者监测，需要整合多模态数据与多领域专业知识。此外，孤立的风险估计无法为预测提供充分的潜在解释。为实现进展评估工作流程的自动化、减少人工工作量，同时提供可解释的结果与支持证据，我们提出OA-MAP，这是一个用于基于证据评估KOA结构与疼痛进展的自主多智能体框架。该系统包含模态专用智能体，包括MRI智能体、X射线智能体和临床智能体，以及一个协调智能体。该框架可根据用户请求与可用患者信息，自主招募专业智能体、选择预测与分析工具，并检索文献作为外部证据。一种不确定性感知的人在回路机制使临床医生能够审查并修正中间结果，触发受影响结果的重新计算。我们使用来自FNIH骨关节炎生物标志物联盟队列的600名参与者评估预测模型，在100名参与者的测试集上，融合模型在结构进展上的AUROC为0.80，在疼痛进展上的AUROC为0.68。案例研究展示了OA-MAP如何结合风险估计、中间结果、跨模态冲突、文献支持与不确定性指标，以支持交互式审查。
英文摘要
Knee osteoarthritis (KOA) progression prediction can support patient monitoring, requiring the integration of multimodal data and multidomain expertise. Moreover, isolated risk estimates provide limited insight underlying a prediction. To automate the progression assessment workflow and reduce manual effort while providing interpretable findings and supporting evidence, we present OA-MAP, an autonomous multi-agent framework for evidence-grounded assessment of structural and pain progression in KOA. The system incorporates modality-specific agents including MRI, X-ray, and clinical agents, together with a coordinator agent. This framework can autonomously recruit specialist agents, select tools for prediction and analysis, and retrieve literature as external evidence based on user request and available patient information. An uncertainty-informed human-in-the-loop mechanism enables clinicians to review and correct intermediate findings, triggering recomputation of affected results. We evaluate the prediction models using 600 participants from the FNIH Osteoarthritis Biomarkers Consortium cohort. On the test set of 100 participants, the fusion models achieve AUROCs of 0.80 for structural progression and 0.68 for pain progression. A case study illustrates how OA-MAP combines risk estimates with intermediate findings, cross-modal conflicts, literature support, and uncertainty indicators to support interactive review.
