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

Andrea Moglia (Lead / Corresponding author), Luca Morelli, Roberto D'Ischia, Lorenzo Maria Fatucchi, Valentina Pucci, Raffaella Berchiolli, Mauro Ferrari, Alfred Cuschieri

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)
62 Downloads (Pure)

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.

Methods: 176 medical students, without prior experience with surgical simulators, were trained to reach proficiency in five tasks on a virtual simulator for RAS. Ensemble deep neural networks (DNN) models were developed and compared with other ensemble AI algorithms, i.e., random forests and gradient boosted regression trees (GBRT).

Results: DNN models achieved a higher accuracy than random forests and GBRT in predicting time to proficiency, 0.84 vs. 0.70 and 0.77, respectively (Peg board 2), 0.83 vs. 0.79 and 0.78 (Ring walk 2), 0.81 vs 0.81 and 0.80 (Match board 1), 0.79 vs. 0.75 and 0.71 (Ring and rail 2), and 0.87 vs. 0.86 and 0.84 (Thread the rings 2). Ensemble DNN models outperformed random forests and GBRT in predicting number of attempts to proficiency, with an accuracy of 0.87 vs. 0.86 and 0.83, respectively (Peg board 2), 0.89 vs. 0.88 and 0.89 (Ring walk 2), 0.91 vs. 0.89 and 0.89 (Match board 1), 0.89 vs. 0.87 and 0.83 (Ring and rail 2), and 0.96 vs. 0.94 and 0.94 (Thread the rings 2).

Conclusions: Ensemble DNN models can identify at an early stage the acquisition rates of surgical technical proficiency of trainees and identify those struggling to reach the required expected proficiency level.

Original languageEnglish
Pages (from-to)6473-6479
Number of pages7
JournalSurgical Endoscopy
Volume36
Early online date12 Jan 2022
DOIs
Publication statusPublished - 12 Jan 2022

Keywords

  • Artificial intelligence robotic surgery
  • Artificial intelligence surgical simulation
  • Deep-learning robotic surgery
  • Deep-learning surgical simulation
  • Machine learning robotic surgery
  • Machine learning surgical simulation

ASJC Scopus subject areas

  • Surgery

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