Ensemble deep learning for the prediction of proficiency at a virtual simulator for robot-assisted surgery.

Journal: Surgical endoscopy
Published Date: Jan 12, 2022

Abstract

BACKGROUND: Artificial intelligence (AI) has the potential to enhance patient safety in surgery, and all its aspects, including education and training, will derive considerable benefit from AI. In the present study, deep-learning models were used to predict the rates of proficiency acquisition in robot-assisted surgery (RAS), thereby providing surgical programs directors information on the levels of the innate ability of trainees to facilitate the implementation of flexible personalized training.

Authors

  • Andrea Moglia
    Department of ElectronicsInformation and BioengineeringPolitecnico di Milano 20133 Milan Italy.
  • Luca Morelli
    General Surgery Unit, Department of Oncology, Transplantation and New Technologies, University of Pisa, Via Paradisa 2, 56124, Pisa, Italy.
  • Roberto D'Ischia
    General Surgery Unit, Cisanello Teaching Hospital of Pisa, 56124, Pisa, Italy.
  • Lorenzo Maria Fatucchi
    General Surgery Unit, Cisanello Teaching Hospital of Pisa, 56124, Pisa, Italy.
  • Valentina Pucci
    General Surgery Unit, Cisanello Teaching Hospital of Pisa, 56124, Pisa, Italy.
  • Raffaella Berchiolli
    Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Vascular Surgery Unit, Pisa, Italy.
  • Mauro Ferrari
  • Alfred Cuschieri
    Surgical Technology and Robotics Group, Institute for Medical Science and Technology (IMSaT), University of Dundee, Dundee, DD2 1FD, UK. a.cuschieri@dundee.ac.uk.

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