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.

翻译：尽管手术技能的提升通常与患者预后改善相关，但其评估过程具有主观性强、劳动密集且需要领域专业知识的特点。自动化数据驱动指标能够缓解这些困难，正如现有微创手术中的机器学习器械追踪模型所展示的那样。然而，这些模型仅在有限的腹腔镜手术数据集上进行过测试，且主要关注孤立任务和机器人手术。本文引入了一个新的公共数据集，聚焦于模拟手术，以内镜垂体手术的鼻腔阶段作为范例。模拟手术提供了一个真实且可重复的环境，这意味着自动化评估所获得的洞察可供新手外科医生在进入真实手术前，于模拟器上磨练技能。PRINTNet（垂体实时器械追踪网络）被创建作为此类自动化评估的基线模型。该模型由用于分类与分割的DeepLabV3、用于追踪的StrongSORT以及用于实现实时性能的NVIDIA Holoscan SDK构成，PRINTNet以每秒22帧的运行速度实现了71.9%的多目标追踪精度。利用此追踪输出，一个多层感知机在预测手术技能水平（新手或专家）方面达到了87%的准确率，其中“总手术时间与器械可见时间之比”与更高的手术技能水平相关。这因此证明了在模拟内镜垂体手术中进行自动化手术技能评估的可行性。新的公开数据集可在此处获取：https://doi.org/10.5522/04/26511049。