Road to automating robotic suturing skills assessment: Battling mislabeling of the ground truth.

Journal: Surgery
Published Date: Sep 16, 2021

Abstract

OBJECTIVE: To automate surgeon skills evaluation using robotic instrument kinematic data. Additionally, to implement an unsupervised mislabeling detection algorithm to identify potentially mislabeled samples that can be removed to improve model performance.

Authors

  • Andrew J Hung
    Center for Robotic Simulation & Education, Catherine & Joseph Aresty Department of Urology, University of Southern California Institute of Urology, Los Angeles, California. Electronic address: Andrew.Hung@med.usc.edu.
  • Sirisha Rambhatla
    Computer Science Department, University of Southern California, Los Angeles, CA, U.S.A.
  • Daniel I Sanford
    Center for Robotic Simulation & Education, Catherine & Joseph Aresty Department of Urology, USC Institute of Urology, University of Southern California, Los Angeles, CA, USA.
  • Nilay Pachauri
    Computer Science Department, Viterbi School of Engineering, University of Southern California, Los Angeles, CA.
  • Erik Vanstrum
    Center for Robotic Simulation and Education, Catherine and Joseph Aresty Department of Urology, USC Institute of Urology, University of Southern California, Los Angeles, CA.
  • Jessica H Nguyen
    Center for Robotic Simulation & Education, Catherine & Joseph Aresty Department of Urology, USC Institute of Urology, University of Southern California, Los Angeles, CA.
  • Yan Liu
    Department of Clinical Microbiology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, 200072, People's Republic of China.

Keywords

External Resources

Popular Topics
Recent Journals