In recent years, the potential applications of machine learning to Minimally Invasive Surgery (MIS) have spurred interest in data sets that can be used to develop data-driven tools. This paper introduces a novel dataset recorded during ex vivo pseudo-cholecystectomy procedures on pig livers, utilizing the da Vinci Research Kit (dVRK). Unlike current datasets, ours bridges a critical gap by offering not only full kinematic data but also capturing all pedal inputs used during the procedure and providing a time-stamped record of the endoscope's movements. Contributed by seven surgeons, this data set introduces a new dimension to surgical robotics research, allowing the creation of advanced models for automating console functionalities. Our work addresses the existing limitation of incomplete recordings and imprecise kinematic data, common in other datasets. By introducing two models, dedicated to predicting clutch usage and camera activation, we highlight the dataset's potential for advancing automation in surgical robotics. The comparison of methodologies and time windows provides insights into the models' boundaries and limitations.

翻译：近年来，机器学习在微创手术（MIS）中的潜在应用激发了人们对可用于开发数据驱动工具的数据集的兴趣。本文介绍了一种新颖的数据集，该数据集记录于离体猪肝假性胆囊切除术过程中，采用达芬奇研究套件（dVRK）采集。与现有数据集不同，我们的数据集不仅提供完整的运动学数据，还捕获了手术过程中使用的所有脚踏输入，并提供了内窥镜运动的时间戳记录，从而弥合了关键缺口。该数据集由七位外科医生贡献，为手术机器人研究引入了新维度，使得创建用于自动化控制台功能的先进模型成为可能。我们的工作解决了其他数据集中常见的记录不完整和运动学数据不精确的现有局限。通过引入两个分别用于预测离合器使用和摄像头激活的模型，我们突显了该数据集在推进手术机器人自动化方面的潜力。对方法和时间窗口的比较分析揭示了模型的边界和局限性。