RAG-RUSS：一种增强检索的机器人超声用于自主颈动脉检查
RAG-RUSS: A Retrieval-Augmented Robotic Ultrasound for Autonomous Carotid Examination
AI总结：
RAG-RUSS通过可解释框架实现自主颈动脉检查，结合检索增强生成提升泛化能力，减少对大规模数据的依赖。
摘要：
机器人超声（US）最近引起了越来越多的关注，作为一种克服传统超声检查局限性的手段，例如强操作者依赖性。然而，现有方法的决策过程往往要么是基于规则的，要么依赖于端到端学习模型，这些模型在操作上如同黑箱。这被视为临床接受的主要限制，并且在常规实践中广泛采用时引发了安全问题。为了解决这一挑战，我们引入了RAG-RUSS，一种可解释的框架，能够按照临床工作流程完成完整的颈动脉检查，同时明确解释当前阶段和下一步计划的动作。此外，鉴于医学数据的稀缺性，我们结合了检索增强生成以提高泛化能力和减少对大规模训练数据集的依赖。该方法在28名志愿者获取的数据上进行训练，而另外四组体积扫描数据来自之前未见过的志愿者，用于测试。结果表明，该方法能够解释当前的扫描阶段，并自主规划探头运动以完成颈动脉检查，包括横切面和纵切面。
英文摘要：
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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