Accurate and interpretable evaluation of surgical skills from kinematic data using fully convolutional neural networks.

Journal: International journal of computer assisted radiology and surgery
Published Date:

Abstract

PURPOSE: Manual feedback from senior surgeons observing less experienced trainees is a laborious task that is very expensive, time-consuming and prone to subjectivity. With the number of surgical procedures increasing annually, there is an unprecedented need to provide an accurate, objective and automatic evaluation of trainees' surgical skills in order to improve surgical practice.

Authors

  • Hassan Ismail Fawaz
    IRIMAS, Université Haute Alsace, 12 Rue des Frères Lumière, 68093, Mulhouse, France. hassan.ismail-fawaz@uha.fr.
  • Germain Forestier
  • Jonathan Weber
    IRIMAS, Université Haute Alsace, 12 Rue des Frères Lumière, 68093, Mulhouse, France.
  • Lhassane Idoumghar
    IRIMAS, Université Haute Alsace, 12 Rue des Frères Lumière, 68093, Mulhouse, France.
  • Pierre-Alain Muller
    IRIMAS, Université Haute Alsace, 12 Rue des Frères Lumière, 68093, Mulhouse, France.