用实时器械追踪自动评估内镜垂体手术技能，助力新手练手。
Automated Surgical Skill Assessment in Endoscopic Pituitary Surgery using Real-time Instrument Tracking on a High-fidelity Bench-top Phantom
- 基于深度学习与实时追踪，构建可量化手术动作的评估系统。
- 在22帧/秒下实现71.9%的多目标追踪精度，87%技能等级分类准确率。
- 公开数据集支持新手模拟训练，推动精准外科培训落地。
手术技能提升通常关联患者预后改善，但当前评估主观性强、耗时且需专业经验。数据驱动的自动化评估可缓解此问题，已有机器学习模型在微创手术中实现器械追踪。然而，现有研究多集中于腹腔镜手术的有限数据集，且侧重孤立任务或机器人手术。本文提出一个新公共数据集，聚焦模拟内镜垂体手术的鼻腔阶段。模拟手术提供真实且可重复的环境，使自动化评估结果可用于新手在模拟器上打磨技能，再过渡至真实手术。为此构建了PRINTNet（垂体实时器械追踪网络），包含DeepLabV3用于分类分割、StrongSORT用于追踪，以及NVIDIA Holoscan SDK保障实时性能。PRINTNet在22帧/秒下实现71.9%的多目标追踪精度。基于该追踪输出，多层感知机以87%准确率预测手术技能水平（新手或专家），其中“总操作时间与器械可见时间之比”与更高技能水平相关。这验证了在模拟内镜垂体手术中实现自动化技能评估的可行性。新数据集已公开：https://doi.org/10.5522/04/26511049。
原文摘要 · Abstract (English)
Improved surgical skill is generally associated with improved patient outcomes, although assessment is subjective; labour-intensive; and requires domain specific expertise. Automated data driven metrics can alleviate these difficulties, as demonstrated by existing machine learning instrument tracking models in minimally invasive surgery. However, these models have been tested on limited datasets of laparoscopic surgery, with a focus on isolated tasks and robotic surgery. In this paper, a new public dataset is introduced, focusing on simulated surgery, using the nasal phase of endoscopic pituitary surgery as an exemplar. Simulated surgery allows for a realistic yet repeatable environment, meaning the insights gained from automated assessment can be used by novice surgeons to hone their skills on the simulator before moving to real surgery. PRINTNet (Pituitary Real-time INstrument Tracking Network) has been created as a baseline model for this automated assessment. Consisting of DeepLabV3 for classification and segmentation; StrongSORT for tracking; and the NVIDIA Holoscan SDK for real-time performance, PRINTNet achieved 71.9% Multiple Object Tracking Precision running at 22 Frames Per Second. Using this tracking output, a Multilayer Perceptron achieved 87% accuracy in predicting surgical skill level (novice or expert), with the "ratio of total procedure time to instrument visible time" correlated with higher surgical skill. This therefore demonstrates the feasibility of automated surgical skill assessment in simulated endoscopic pituitary surgery. The new publicly available dataset can be found here: https://doi.org/10.5522/04/26511049.
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