Laparoscopic surgery is a complex surgical technique that requires extensive training. Recent advances in deep learning have shown promise in supporting this training by enabling automatic video-based assessment of surgical skills. However, the development and evaluation of deep learning models is currently hindered by the limited size of available annotated datasets. To address this gap, we introduce the Laparoscopic Skill Analysis and Assessment (LASANA) dataset, comprising 1270 stereo video recordings of four basic laparoscopic training tasks. Each recording is annotated with a structured skill rating, aggregated from three independent raters, as well as binary labels indicating the presence or absence of task-specific errors. The majority of recordings originate from a laparoscopic training course, thereby reflecting a natural variation in the skill of participants. To facilitate benchmarking of both existing and novel approaches for video-based skill assessment and error recognition, we provide predefined data splits for each task. Furthermore, we present baseline results from a deep learning model as a reference point for future comparisons.

翻译：腹腔镜手术是一种复杂的外科技术，需要大量训练。深度学习的最新进展通过实现基于视频的手术技能自动评估，为支持此类训练展现出潜力。然而，当前可用标注数据集的规模有限，阻碍了深度学习模型的开发与评估。为填补这一空白，我们提出了腹腔镜技能分析与评估（LASANA）数据集，该数据集包含四项基本腹腔镜训练任务的1270个立体视频记录。每个记录均标注有结构化技能评分（由三位独立评分者综合得出）以及指示任务特定错误存在与否的二元标签。大多数记录源自腹腔镜培训课程，因而反映了参与者技能的自然差异。为便于对现有及新颖的基于视频技能评估与错误识别方法进行基准测试，我们为每项任务提供了预定义的数据划分。此外，我们展示了一个深度学习模型的基线结果，作为未来比较的参考基准。