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In Journal of robotic surgery

Surgical education courses and certification tests require human evaluators to assess performance. Deep neural network (DNN) methods include techniques for classifying the content of videos which may enable automated scoring of video performance. Researchers collected 254 videos of two simulation-based exercises performed by attending surgeons. The performance in each video was scored by experienced instructors and converted into three class labels-expert, intermediate, and novice. The videos were cut into 2227 10 s clips for training DNNs in the Google Video Intelligence AutoML service. The DNN models matched the classifications applied by human evaluators with 83.1% accuracy for the Ring & Rail exercise and 80.8% for the Suture Sponge exercise. DNN models trained on individual exercises delivered very good results (80 + % accuracy) in matching the classifications assigned by human instructors and may eventually be able to supplement or replace human evaluators.

Smith Roger, Julian Danielle, Dubin Ariel


Assessment, Deep neural network, Machine learning, Minimally invasive surgery, Robotic surgery, Simulation, Surgical education