Machine learning methods for automated technical skills assessment with instructional feedback in ultrasound-guided interventions.

Journal: International journal of computer assisted radiology and surgery
Published Date: Apr 20, 2019

Abstract

OBJECTIVE: Currently, there is a worldwide shift toward competency-based medical education. This necessitates the use of automated skills assessment methods during self-guided interventions training. Making assessment methods that are transparent and configurable will allow assessment to be interpreted into instructional feedback. The purpose of this work is to develop and validate skills assessment methods in ultrasound-guided interventions that are transparent and configurable.

Authors

  • Matthew S Holden
    Laboratory for Percutaneous Surgery, School of Computing, Queen's University, Kingston, ON, Canada. 72mh@queensu.ca.
  • Sean Xia
    Laboratory for Percutaneous Surgery, School of Computing, Queen's University, Kingston, ON, Canada.
  • Hillary Lia
    Laboratory for Percutaneous Surgery, School of Computing, Queen's University, Kingston, ON, Canada.
  • Zsuzsanna Keri
    Laboratory for Percutaneous Surgery, School of Computing, Queen's University, Kingston, ON, Canada.
  • Colin Bell
    Department of Emergency Medicine, School of Medicine, Queen's University, Kingston, ON, Canada.
  • Lindsey Patterson
    Department of Anesthesiology and Perioperative Medicine, School of Medicine, Queen's University, Kingston, ON, Canada.
  • Tamas Ungi
    Laboratory for Percutaneous Surgery, School of Computing, Queen's University, Kingston, ON, Canada.
  • Gabor Fichtinger
    Department of Mechanical and Material Engineering, Queen's University, Kingston, ON, Canada.

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