ArXiv Preprint
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.
Aneeq Zia, Kiran Bhattacharyya, Xi Liu, Ziheng Wang, Max Berniker, Satoshi Kondo, Emanuele Colleoni, Dimitris Psychogyios, Yueming Jin, Jinfan Zhou, Evangelos Mazomenos, Lena Maier-Hein, Danail Stoyanov, Stefanie Speidel, Anthony Jarc
2022-12-08
