Toward Personalized Training and Skill Assessment in Robotic Minimally Invasive Surgery

• Published in: Lecture notes in engineering and computer science Vol. 2; pp. 719 - 724
• Main Authors: Fard, Mahtab J, Ameri, Sattar, Ellis, R Darin
• Format: Journal Article
• Language: English
• Published: 21.10.2016
• Subjects: Assessments; Mathematical models; Parameters; Personalized; Skills; Telesurgery; Training; Tuning
• ISBN: 9881404827; 9789881404824
• ISSN: 2078-0958; 2078-0966

Despite the immense technology advancement in the surgeries the criteria of assessing the surgical skills still remains based on subjective standards. With the advent of robotic-assisted minimally invasive surgery (RMIS), new opportunities for objective and autonomous skill assessment is introduced. Previous works in this area are mostly based on structured-based method such as Hidden Markov Model (HMM) which need enormous pre-processing. In this study, in contrast with them, we develop a new shaped-based framework for automatically skill assessment and personalized surgical training with minimum parameter tuning. Our work has addressed main aspects of skill evaluation; develop gesture recognition model directly on temporal kinematic signal of robotic-assisted surgery, and build automated personalized RMIS gesture training framework which. We showed that our method, with an average accuracy of 82% for suturing, 70% for needle passing and 85% for knot tying, performs better or equal than the state-of-the-art methods, while simultaneously needs minimum pre-processing, parameter tuning and provides surgeons with online feedback for their performance during training.