A benchmark for video-based laparoscopic skill analysis and assessment

TL;DR

This paper introduces LASANA, a new large dataset of annotated laparoscopic videos, to support the development and benchmarking of deep learning models for surgical skill assessment and error detection.

Contribution

The paper provides the first comprehensive, annotated laparoscopic video dataset with structured skill ratings and error labels, enabling standardized benchmarking for skill analysis models.

Findings

Baseline deep learning model performance established

Dataset reflects natural variation in surgical skill levels

Predefined data splits facilitate fair comparison of methods

Abstract

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.