Enhancing Skill Assessment of Autonomous Robot-Assisted Minimally Invasive Surgery: A Comprehensive Analysis of Global and Gesture-Level Techniques applied on the JIGSAWS Dataset

Abstract

Improved surgical skills play a crucial role in ensuring optimal patient outcomes.Traditional methods for skill assessment include self-rating questionnaires and expert evaluations, but these approaches are prone to bias and require substantial qualified human resources.The emergence of Surgical Data Science (SDS) offers a promising avenue for automating skill assessment, leveraging data science techniques to capture, organize, analyze, and model surgical data.In this paper, kinematic data was employed from the JIGSAWS -which is the only skill-annotated Robot-Assisted Minimally Invasive Surgery (RAMIS) dataset -to classify surgeons into novice and experienced groups, using various classification methods (Decision Tree, k-Nearest Neighbors, Support Vector Machines, Logistic Regression, Dynamic Time Warping, and 1D Convolutional Neural Network).The research encompasses a thorough analysis of parameter tuning and dimensional reduction techniques with the aim of establishing a universal benchmark for data classification.The surgical training tasks of suturing, knot-tying and needle-passing consistently achieved 100 % accuracy.The accuracy attained during surgical gesture analysis often exceeded the overall accuracy of the global assessment of the dataset.