RAUL：参考辅助输尿管镜定位用于技能评估
RAUL: Reference-Assisted Ureteroscopy Localization for Skill Assessment
查看机构详情
- Vanderbilt University（范德堡大学）
- Vanderbilt University Medical Center（范德堡大学医学中心）
机构由 AI 辅助整理，请以论文原文为准。
浏览论文内容
中文总结 AI 辅助
RAUL通过参考辅助重建从输尿管镜视频恢复轨迹，提高定位覆盖率，实现无需额外设备的客观技能评估。
中文摘要 AI 辅助
目的：在输尿管镜肾结石手术中，对解剖结构的不完全导航可能导致重复干预。虽然熟练的外科医生再干预率较低，但目前尚无客观指标来量化镜体导航性能，以评估受训者何时达到熟练水平。本研究旨在从内镜视频中恢复输尿管镜轨迹，并推导导航指标以量化技能差异。方法：我们提出RAUL，一种参考辅助重建框架，仅通过模型中的输尿管镜视频恢复输尿管镜轨迹。对于每个模型，我们使用一段缓慢、高质量参考探索视频生成参考重建，并将后续探索视频相对于该参考进行定位。我们对照电磁跟踪的镜体姿态评估定位精度，并从模型探索轨迹计算导航指标，以比较不同经验水平的外科住院医师。结果：所提出的参考辅助框架在9个模型上实现了平均平移均方根误差为$0.5 \pm 0.1$毫米。与标准运动恢复结构（SfM）相比，所提出的流程将逐帧定位覆盖率从所有视频帧的$50.5 \pm 14.9\\%$提高到$86.1 \pm 7.2\\%$。重建轨迹在既定导航指标中揭示了高经验与低经验受训者之间的显著差异。结论：与标准SfM流程相比，RAUL能够从视频中实现更完整的输尿管镜轨迹恢复，从而无需额外跟踪设备即可进行基于轨迹的技能评估。意义：据我们所知，这是首次在没有外部跟踪传感器的情况下仅使用视频恢复输尿管镜轨迹用于技能评估，支持输尿管镜导航技能的可扩展自动化评估。
英文摘要
Objective: Incomplete navigation of anatomy during ureteroscopic kidney stone surgeries can contribute to repeat interventions. While skilled surgeons have lower reintervention rates, there are no objective metrics to quantify scope-navigation performance to evaluate when a trainee becomes skilled. This work aims to recover ureteroscope trajectories from endoscopic video and derive navigation metrics to quantify differences in skill. Methods: We propose RAUL, a reference-assisted reconstruction framework for recovering ureteroscope trajectories from ureteroscope videos only in phantoms. For each phantom, we use a slow, high-quality reference exploration video to generate a reference reconstruction. We localize subsequent exploration videos against this reference. We evaluate localization accuracy against electromagnetically tracked scope pose. We compute navigation metrics from phantom exploration trajectories to compare surgical residents across experience levels. Results: The proposed reference-assisted framework achieves a mean translation root mean square error of $0.5 \pm 0.1$ mm across 9 phantoms. Compared to standard Structure-from-Motion (SfM), the proposed pipeline increases frame-wise localization coverage from $50.5 \pm 14.9\%$ to $86.1 \pm 7.2\%$ of all video frames. The reconstructed trajectories revealed significant differences between high- and low-experience trainees in established navigation metrics. Conclusion: RAUL enables substantially more complete recovery of ureteroscope trajectories from videos compared to standard SfM pipelines, enabling trajectory-based skill assessment without additional tracking equipment. Significance: To the best of our knowledge, this is the first use of video-only recovery of ureteroscope trajectories without external tracking sensors for skill assessment, supporting scalable automated assessment of ureteroscopy navigation skill.
