A benchmark for video-based laparoscopic skill analysis and assessment
This work addresses a bottleneck in developing deep learning models for surgical training by providing a standardized dataset, but it is incremental as it focuses on data creation rather than novel methods.
The authors tackled the problem of limited annotated datasets for video-based assessment of laparoscopic surgical skills by introducing the LASANA dataset, which includes 1270 stereo video recordings with skill ratings and error labels, and provided baseline results from a deep learning model for benchmarking.
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.