University of Minnesota
University of Minnesota: Department of Mechanical Engineering
http://www.me.umn.edu/
  • Mechanical Engineering

 

Surgical Skill Evaluation

Tool Tracking   poly  poly

Most automated surgical skill evaluation methods focus predominantly on diagnosis using task-specific maneuvers. These maneuvers require surgical expertise to identify and are observed over the course of a full task. The aim of this investigation is to propose features for automated skill evaluation that are relevant regardless of the surgical training task the tools perform. A secondary aim goal is to diagnose skill without requiring the complete time series of data from a given trial.

We are also investigating the importance of pre-procedural warm-up in surgery via the Robotic Readiness Study.

Our research questions include:

  • How do we quantify the technical skills of a training or practicing surgeon?
  • What is the relative importance of having tool motion trajectories vs full video data when measuring surgical skill?
  • What is it about the dynamics of tool motion that implicates skilled vs non-skilled motions?
  • What feedback can most efficiently improve surgical skills?

The current researcher for this project is Anna French.

Relevant Papers:

[1] Anna French, Thomas S Lendvay, Robert M Sweet, and Timothy M Kowalewski. Predicting surgical skill from the first n seconds of a task: value over task time using the isogony principle. International Journal of Computer Assisted Radiology and Surgery, 12(7):1161--1170, July 2017. [ DOI | http ]
[2] Anna French and Timothy M. Kowalewski. Laparoscopic skill classification using the two-third power law and the isogony principle. ASME Journal of Medical Devices, 2017. [ .pdf ]
[3] Thomas S Lendvay, Lee White, and Timothy Kowalewski. Crowdsourcing to assess surgical skill. JAMA Surgery: the Journal of the American Medical Association - Surgery, 150(11):1086--1087, 2015. [ .pdf ]
[4] Lee W White, Timothy M Kowalewski, Rodney Lee Dockter, Bryan Comstock, Blake Hannaford, and Thomas S Lendvay. Crowd-sourced assessment of technical skill: A valid method for discriminating basic robotic surgery skills. Journal of Endourology, 29(11):1295--1301, 2015. [ DOI | http ]
[5] Carolyn Chen, Lee White, Timothy Kowalewski, Rajesh Aggarwal, Chris Lintott, Bryan Comstock, Katie Kuksenok, Cecilia Aragon, Daniel Holst, and Thomas Lendvay. Crowd-sourced assessment of technical skills: a novel method to evaluate surgical performance. Journal of Surgical Research, 2013. [ .pdf ]