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Timely and effective feedback within surgical training plays a critical rolein developing the skills required to perform safe and efficient surgery.Feedback from expert surgeons, while especially valuable in this regard, ischallenging to acquire due to their typically busy schedules, and may besubject to biases. Formal assessment procedures like OSATS and GEARS attempt toprovide objective measures of skill, but remain time-consuming. With advancesin machine learning there is an opportunity for fast and objective automatedfeedback on technical skills. The SimSurgSkill 2021 challenge (hosted as asub-challenge of EndoVis at MICCAI 2021) aimed to promote and foster work inthis endeavor. Using virtual reality (VR) surgical tasks, competitors weretasked with localizing instruments and predicting surgical skill. Here wesummarize the winning approaches and how they performed. Using this publiclyavailable dataset and results as a springboard, future work may enable moreefficient training of surgeons with advances in surgical data science. Thedataset can be accessed fromhttps://console.cloud.google.com/storage/browser/isi-simsurgskill-2021.

Objective Surgical Skills Assessment and Tool Localization: Results from the MICCAI 2021 SimSurgSkill Challenge