OSS: Open Suturing Skills Vision-Based Assessment Challenge 2024-2025

2026-05-21

AI Summary

This work addresses the lack of automated visual assessment methods for surgical skills in open surgery, particularly the challenge of fine-grained quantification in suturing tasks. Through the MICCAI Challenge, we present the first publicly available multimodal benchmark dataset specifically designed for open suturing skill evaluation, integrating GoPro video recordings with instrument motion trajectories to support three core tasks: skill-level classification, OSATS score prediction, and hand/tool tracking. We investigate both general-purpose spatiotemporal video models and hybrid architectures within a multitask learning framework. Our results demonstrate that generic spatiotemporal models achieve superior performance; increasing training data significantly improves accuracy in fine-grained OSATS scoring; however, occlusions and objects exiting the field of view remain key limitations for reliable keypoint tracking in motion analysis.

Abstract

Achieving high levels of surgical skill through effective training is essential for optimal patient outcomes. Automated, data-driven skill assessment holds significant potential to improve surgical training. While machine learning-based methods are increasingly popular for assessing skills in minimally invasive surgery, their application to open surgery remains limited. We present the results of a dedicated MICCAI challenge designed to benchmark and advance vision-based skill assessment in open surgery. The challenge dataset comprises videos of an open suturing training task recorded with a static GoPro camera in a dry-lab setting, with instrument trajectories available in addition to the primary video modality. The OSS Challenge was hosted over two consecutive years, comprising two and three independent tasks, respectively: (1) classifying skill level into four classes, (2) predicting the full Objective Structured Assessment of Technical Skills across eight categories, and (3) tracking hands and surgical tools. Participants submitted diverse solutions including deep learning-based video models, tracking-driven methods, and hybrid approaches. General-purpose spatiotemporal video models consistently achieved the strongest performance, though conceptually diverse approaches reached competitive levels when well-executed. Predicting fine-grained OSATS scores remains challenging but benefits substantially from increased training data. Keypoint tracking proves difficult given frequent occlusions and out-of-frame instances, limiting current applicability for motion-based skill analysis. This work benchmarks innovative and diverse solutions for surgical skill assessment, highlighting both the promise and current limitations of video-based evaluation in open surgery and identifying critical directions for advancing automated skill assessment toward clinical impact.

Problem

• open surgery
• surgical skill assessment
• vision-based evaluation
• suturing skills
• automated assessment

Innovation

• vision-based skill assessment
• open surgery
• surgical video analysis
• OSATS prediction
• instrument tracking