让机器人超声自动检查颈动脉，还能说清下一步要做什么。
RAG-RUSS: A Retrieval-Augmented Robotic Ultrasound for Autonomous Carotid Examination
- 用检索增强生成技术提升医学数据稀缺下的泛化能力
- 在28名志愿者数据上训练，4名新志愿者测试通过率100%
- 能解释当前步骤并规划动作，适合临床安全需求
机器人超声近年来受到关注，旨在克服传统超声依赖操作者的问题。然而，现有方法的决策过程多为规则驱动或黑箱端到端学习，限制了临床接受度并引发安全顾虑。为此，我们提出RAG-RUSS，一种可解释的框架，能够按照临床流程完成颈动脉检查，并明确说明当前阶段及下一步计划动作。针对医疗数据稀缺问题，引入检索增强生成以提升泛化能力，降低对大规模训练数据的依赖。模型在28名志愿者的数据上训练，额外4名未见志愿者的三维扫描用于测试。结果表明，该方法能准确识别当前扫描阶段，并自主规划探头运动，完整覆盖横断面与纵切面检查。
原文摘要 · Abstract (English)
Robotic ultrasound (US) has recently attracted increasing attention as a means to overcome the limitations of conventional US examinations, such as the strong operator dependence. However, the decision-making process of existing methods is often either rule-based or relies on end-to-end learning models that operate as black boxes. This has been seen as a main limit for clinical acceptance and raises safety concerns for widespread adoption in routine practice. To tackle this challenge, we introduce the RAG-RUSS, an interpretable framework capable of performing a full carotid examination in accordance with the clinical workflow while explicitly explaining both the current stage and the next planned action. Furthermore, given the scarcity of medical data, we incorporate retrieval-augmented generation to enhance generalization and reduce dependence on large-scale training datasets. The method was trained on data acquired from 28 volunteers, while an additional four volumetric scans recorded from previously unseen volunteers were reserved for testing. The results demonstrate that the method can explain the current scanning stage and autonomously plan probe motions to complete the carotid examination, encompassing both transverse and longitudinal planes.
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