Machine learning enhances assessment of proficiency in endovascular aortic repair simulations.

Abstract

Authors

• Rebecca Andrea Conradsen Skov
  Department of Vascular Surgery, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark; Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark; Copenhagen Academy for Medical Education and Simulation (CAMES), Denmark. Electronic address: rebecca.andrea.conradsen.skov@regionh.dk.
• Jonathan Lawaetz
  Department of Vascular Surgery, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark; Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark; Copenhagen Academy for Medical Education and Simulation (CAMES), Denmark.
• Michael Strøm
  Department of Vascular Surgery, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark; Copenhagen Academy for Medical Education and Simulation (CAMES), Denmark.
• Isabelle Van Herzeele
  Department of Thoracic and Vascular Surgery, Ghent University Hospital, Ghent, Belgium.
• Lars Konge
  Copenhagen Academy for Medical Education and Simulation (CAMES), Copenhagen, Denmark.
• Timothy Andrew Resch
  Department of Vascular Surgery, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark; Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark.
• Jonas Peter Eiberg
  Department of Vascular Surgery, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark; Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark; Copenhagen Academy for Medical Education and Simulation (CAMES), Denmark.

Keywords

External Resources