Introduction: Surgical skill evaluation relies on either direct observation or video review by humans. Both are time consuming, costly and difficult to perform at a large scale. Machine learning could allow video reviewing to economically viable at a large scale.
We sought to train and evaluate multi-task convolutional neural network to predict surgical proficiency score using a validated robotic assisted suturing model.
Methods: Twenty-three videos of surgeons with varying robotic skill levels completing a validated urethrovesical anastomosis model were utilized. Global evaluation assessment of robotic skills (GEARS) scoring was assigned to each video by expert reviewers and used as training data for the machine learning algorithm. The front wrist joint, rear wrist joint, and needle driver tip of each instrument, and needle, were manually labelled in 300 frames to train a YOLO object detector (Figure 1). Each video was randomly broken down into 6 second clips, providing multiple representative clips for each video. A heatmap representing object movement based on coordinate differences over time was generated (Figure 2). Using a multi-task convolutional neural network, each clip was assigned a GEARS score. A composite GEARS score for each video was then calculated using a novel video pooling method. Model training was performed using leave-one-out cross validation. The output was the predicted score for each GEARS domain. Success was defined when the model accurately predicted the domain score within the range assigned by human experts.
Results: Algorithm training for 23 videos of 1-2 minute duration took 4 hours using a supercomputer. The multi-task model successfully predicted 6 of 6 domains in 65% of videos, 5 of 6 domains in 74% of videos and 4 of 6 domains in 74% of videos.
Conclusions: With limited training data, a novel multi-task convolutional neural network can accurately predict surgical proficiency as assessed by GEARS scoring in a urethrovesical model. Further testing using actual operative videos is warranted. Source of