Endovascular interventions are a life-saving treatment for many diseases, yet suffer from drawbacks such as radiation exposure and potential scarcity of proficient physicians. Robotic assistance during these interventions could be a promising support towards these problems. Research focusing on autonomous endovascular interventions utilizing artificial intelligence-based methodologies is gaining popularity. However, variability in assessment environments hinders the ability to compare and contrast the efficacy of different approaches, primarily due to each study employing a unique evaluation framework. In this study, we present deep reinforcement learning-based autonomous endovascular device navigation on three distinct digital benchmark interventions: BasicWireNav, ArchVariety, and DualDeviceNav. The benchmark interventions were implemented with our modular simulation framework stEVE (simulated EndoVascular Environment). Autonomous controllers were trained solely in simulation and evaluated in simulation and on physical test benches with camera and fluoroscopy feedback. Autonomous control for BasicWireNav and ArchVariety reached high success rates and was successfully transferred from the simulated training environment to the physical test benches, while autonomous control for DualDeviceNav reached a moderate success rate. The experiments demonstrate the feasibility of stEVE and its potential for transferring controllers trained in simulation to real-world scenarios. Nevertheless, they also reveal areas that offer opportunities for future research. This study demonstrates the transferability of autonomous controllers from simulation to the real world in endovascular navigation and lowers the entry barriers and increases the comparability of research on endovascular assistance systems by providing open-source training scripts, benchmarks and the stEVE framework.

翻译：血管内介入是治疗多种疾病的重要方法，但存在辐射暴露及熟练医师可能短缺等缺陷。在这些介入过程中引入机器人辅助有望为解决上述问题提供支持。采用基于人工智能方法实现血管内介入自主化的研究正日益受到关注。然而，由于各项研究采用不同的评估框架，评估环境的差异性阻碍了不同方法有效性的比较与对照。本研究提出基于深度强化学习的血管内器械自主导航方法，并在三个不同的数字化基准介入任务中进行验证：BasicWireNav、ArchVariety 和 DualDeviceNav。这些基准介入任务通过我们开发的模块化仿真框架 stEVE（模拟血管内环境）实现。自主控制器仅在仿真环境中训练，并在仿真环境及配备摄像头与荧光透视反馈的物理测试平台上进行评估。BasicWireNav 与 ArchVariety 的自主控制达到了较高的成功率，并能成功从仿真训练环境迁移至物理测试平台；而 DualDeviceNav 的自主控制取得了中等成功率。实验证明了 stEVE 的可行性及其在将仿真训练控制器迁移至真实场景方面的潜力，同时也揭示了未来可进一步探索的研究方向。本研究展示了血管内导航中自主控制器从仿真环境到真实世界的可迁移性，并通过开源训练脚本、基准测试及 stEVE 框架，降低了血管内辅助系统研究的入门门槛，提升了研究的可比性。