Surgical skill levels: Classification and analysis using deep neural network model and motion signals.

Journal: Computer methods and programs in biomedicine
Published Date: May 13, 2019

Abstract

BACKGROUND AND OBJECTIVES: Currently, the assessment of surgical skills relies primarily on the observations of expert surgeons. This may be time-consuming, non-scalable, inconsistent and subjective. Therefore, an automated system that can objectively identify the actual skills level of a junior trainee is highly desirable. This study aims to design an automated surgical skills evaluation system.

Authors

  • Xuan Anh Nguyen
    Department of Mechanical and Aerospace Engineering, Monash University, Clayton, Victoria, 3800, Australia.
  • Damir Ljuhar
    Department of Surgical Simulation, Monash Children's Hospital, Melbourne, Australia; Department of Paediatrics, School of Clinical Sciences, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia.
  • Maurizio Pacilli
    Department of Surgical Simulation, Monash Children's Hospital, Melbourne, Australia; Department of Paediatrics, School of Clinical Sciences, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia.
  • Ramesh Mark Nataraja
    Department of Surgical Simulation, Monash Children's Hospital, Melbourne, Australia; Department of Paediatrics, School of Clinical Sciences, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia.
  • Sunita Chauhan
    Department of Mechanical and Aerospace Engineering, Monash University, Clayton, Victoria, 3800, Australia. Electronic address: Sunita.Chauhan@monash.edu.

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