Timely and effective feedback within surgical training plays a critical role in developing the skills required to perform safe and efficient surgery. Feedback from expert surgeons, while especially valuable in this regard, is challenging to acquire due to their typically busy schedules, and may be subject to biases. Formal assessment procedures like OSATS and GEARS attempt to provide objective measures of skill, but remain time-consuming. With advances in machine learning there is an opportunity for fast and objective automated feedback on technical skills. The SimSurgSkill 2021 challenge (hosted as a sub-challenge of EndoVis at MICCAI 2021) aimed to promote and foster work in this endeavor. Using virtual reality (VR) surgical tasks, competitors were tasked with localizing instruments and predicting surgical skill. Here we summarize the winning approaches and how they performed. Using this publicly available dataset and results as a springboard, future work may enable more efficient training of surgeons with advances in surgical data science. The dataset can be accessed from https://console.cloud.google.com/storage/browser/isi-simsurgskill-2021.
翻译:在外科培训中,及时有效的反馈在培养安全高效的外科手术所需的技能方面发挥着关键作用。专家外科医生的反馈,虽然在这方面特别宝贵,但由于通常繁忙的日程安排,很难获得反馈,而且可能受到偏见的影响。正式的评估程序,如OSSATS和GEARS,试图提供客观的技能衡量标准,但是仍然耗费时间。随着机器学习的进展,有机会对技术技能进行快速和客观的自动反馈。SimSurgSkil 2021挑战(作为EndoVis在MICCAI 2021的次级挑战主办)旨在推动和推动这项工作。利用虚拟现实(VR)外科手术任务,竞争者的任务是使工具本地化和预测外科技能。在这里,我们总结胜出的方法及其表现。利用这一公开的数据集和结果作为跳板,未来的工作可以使外科医生获得更有效的外科科学进步培训。数据集可从https://console.cloud.gole.com/storrowser/is-simsurgille-2021访问。