OSS2024

高效的手术技巧对患者的预后至关重要。虽然基于机器学习的手术技能评估在微创手术中越来越受欢迎，但在开放手术中却相对较少。开放手术的自由度更高，使得评估更为复杂。通过提供实时、数据驱动和客观的手术表现评估，自动化技能评估可以显著改善手术技能培训。该数据集包含模拟环境中手术缝合的视频，每个视频根据OSATS类别和全局评级分数（GRS）进行评级。参与者需要完成的任务包括：1) 预测GRS总分；2) 预测整个OSATS计分表。这将帮助外科医生更好地练习和改进基本的手术技能，从而提高手术效果。

Effective surgical techniques are critical to patient outcomes. While machine learning-based surgical skill assessment has gained increasing popularity in minimally invasive surgery, it remains relatively scarce in open surgery. Open surgery features greater degrees of freedom, rendering the assessment process more complex. By delivering real-time, data-driven and objective evaluations of surgical performance, automated skill assessment can significantly improve surgical skill training. This dataset contains videos of surgical suturing in a simulated environment, where each video is rated based on the OSATS categories and the Global Rating Score (GRS). The tasks required of participants include: 1) predicting the total GRS score; 2) predicting the complete OSATS scoring rubric. This will assist surgeons in better practicing and refining fundamental surgical skills, thereby enhancing surgical outcomes.