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

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 fore

SUBMITTER: Moglia A 

PROVIDER: S-EPMC9402513 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature