Development of Machine Learning-Based Assessment System for Laparoscopic Surgical Skills Using Motion-Capture.

Laparoscopic surgery is a widely used surgical technique, on the other hand, its high degree of difficulty makes it difficult for beginners to learn the technique efficiently. In addition, recent working hour restrictions and shortages of surgeons have resulted in insufficient training time, and establishing the efficiently training methods is becoming urgent needs. Therefore, to promote the skill proficiency of novice surgeons, machine learning-based assessment system for laparo-scopic surgical skills was developed. A measurement system with a simple configuration was introduced so that trainees can easily use it alone. Alternatively, the indices related to the opening ratio and the rotation angle of surgical instruments, which were measured in the authors' previous study were no longer available. Therefore, comparative experiments were conducted to verify the effect of the lack of indices related to these data on the accuracy of skill evaluation. Based on the measurement data of 104 wet lab trainings measured in the previous study, a machine learning model that evaluates surgeon's skill at 3 levels based on the number of surgical experiences, and global operative assessment of laparoscopic skills (GOALS) which is a type of surgical skill evaluation index were established. By using the explainable AI method, this system can present the skill evaluation result including its basis to the trainee. Since the developed system can be easily operated by a GUI-based program, trainees can confirm the quantitative evaluation result on-site immediately after the training.