Machine Learning based Classification of Local Robotic Surgical Skills in a Training Tasks Set.

During surgical training, it is important for the surgeon develops good motor skills throughout his training. For this reason, various surgical training systems have been developed to enhance these skills. However, one of the great challenges in these training systems is being able to objectively measure the ability and performance of the main surgical tasks, where currently only a global measurement is obtained once the task is completed. In this work, a temporal evaluation scheme is proposed, that is, an evaluation of local surgical performance at different time intervals during the training of typical tasks (knot-tying, needle-passing and suturing). The goal is to automatically classify expert (experience >100 hrs) and non-expert (experience <10 hrs) surgeons according to their performance during training, based on three classifiers: K-Nearest Neighborhood, Random Forest, and Support Vector Machine Unlike other previously reported methods, this work proposes a new evaluation scheme based on segments or time intervals, which can be an indicator of the surgeon's local performance during a robotic surgical task, without the need for direct labeling of the data at the segment level. The classification performance from obtained results was in accuracy 83% to 100%, 88% to 100% of AUC-ROC, and 88% to 100% of F1-Score in the final test between experts and non-experts surgeons, where the Support Vector Machine classifier presented the best performance. These results suggest that this proposed method by time intervals could be used in various surgical trainers to evaluate the local performance of a surgeon during trainingand thus be able to provide a tool for the quantitative visualization of opportunities to improve surgical skills.Clinical relevance- We consider that the proposed method to carry out a local performance evaluation during surgical training can provide useful information in the learning and improvement of surgical skills.