Assessing system operation skills in robotic surgery trainees

Rajesh Kumar, Amod Jog, Anand Malpani, Balazs Vagvolgyi, David Yuh, Hiep Nguyen, Gregory Hager, Chi Chiung Grace Chen

Research output: Contribution to journalArticlepeer-review


Background: With increased use of robotic surgery in specialties including urology, development of training methods has also intensified. However, current approaches lack the ability to discriminate between operational and surgical skills. Methods: An automated recording system was used to longitudinally (monthly) acquire instrument motion/telemetry and video for four basic surgical skills - suturing, manipulation, transection, and dissection. Statistical models were then developed to discriminate the human-machine skill differences between practicing expert surgeons and trainees. Results: Data from six trainees and two experts was analyzed to validate the first ever statistical models of operational skills, and demonstrate classification with very high accuracy (91.7% for masters, and 88.2% for camera motion) and sensitivity. Conclusions: The paper reports on a longitudinal study aimed at tracking robotic surgery trainees to proficiency, and methods capable of objectively assessing operational and technical skills that would be used in assessing trainee progress at the participating institutions.

Original languageEnglish (US)
Pages (from-to)118-124
Number of pages7
JournalInternational Journal of Medical Robotics and Computer Assisted Surgery
Issue number1
StatePublished - Mar 2012


  • Automated skill assessment
  • Objective skill assessment
  • Robotic surgery training

ASJC Scopus subject areas

  • Surgery
  • Biophysics
  • Computer Science Applications


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