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As part of their training all medical students and residents have to passbasic surgical tasks such as knot tying, needle-passing, and suturing. Theirassessment is typically performed in the operating room by surgical facultywhere mistakes and failure by the student increases the operation time andcost. This evaluation is quantitative and has a low margin of error. Simulationhas emerged as a cost effective option but it lacks assessment or requiresadditional expensive hardware for evaluation. Apps that provide training videoson surgical knot trying are available to students but none have evaluation. Wepropose a cascaded neural network architecture that evaluates a student'sperformance just from a video of themselves simulating a surgical knot tyingtask. Our model converts video frame images into feature vectors with apre-trained deep convolutional network and then models the sequence of frameswith a temporal network. We obtained videos of medical students and residentsfrom the Robert Wood Johnson Hospital performing knot tying on a standardizedsimulation kit. We manually annotated each video and proceeded to do afive-fold cross-validation study on them. Our model achieves a medianprecision, recall, and F1-score of 0.71, 0.66, and 0.65 respectively indetermining the level of knot related tasks of tying and pushing the knot. Ourmean precision score averaged across different probability thresholds is 0.8.Both our F1-score and mean precision score are 8% and 30% higher than that of arecently published study for the same problem. We expect the accuracy of ourmodel to further increase as we add more training videos to the model thusmaking it a practical solution that students can use to evaluate themselves.

A Cascaded Neural Network System For Rating Student Performance In Surgical Knot Tying Simulation