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Ensemble deep learning for the prediction of proficiency at a virtual simulator for robot-assisted surgery
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 ro...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9402513/ https://www.ncbi.nlm.nih.gov/pubmed/35020053 http://dx.doi.org/10.1007/s00464-021-08999-6 |
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author | Moglia, Andrea Morelli, Luca D’Ischia, Roberto Fatucchi, Lorenzo Maria Pucci, Valentina Berchiolli, Raffaella Ferrari, Mauro Cuschieri, Alfred |
author_facet | Moglia, Andrea Morelli, Luca D’Ischia, Roberto Fatucchi, Lorenzo Maria Pucci, Valentina Berchiolli, Raffaella Ferrari, Mauro Cuschieri, Alfred |
author_sort | Moglia, Andrea |
collection | PubMed |
description | 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00464-021-08999-6. |
format | Online Article Text |
id | pubmed-9402513 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-94025132022-08-26 Ensemble deep learning for the prediction of proficiency at a virtual simulator for robot-assisted surgery Moglia, Andrea Morelli, Luca D’Ischia, Roberto Fatucchi, Lorenzo Maria Pucci, Valentina Berchiolli, Raffaella Ferrari, Mauro Cuschieri, Alfred Surg Endosc Article 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00464-021-08999-6. Springer US 2022-01-12 2022 /pmc/articles/PMC9402513/ /pubmed/35020053 http://dx.doi.org/10.1007/s00464-021-08999-6 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Moglia, Andrea Morelli, Luca D’Ischia, Roberto Fatucchi, Lorenzo Maria Pucci, Valentina Berchiolli, Raffaella Ferrari, Mauro Cuschieri, Alfred Ensemble deep learning for the prediction of proficiency at a virtual simulator for robot-assisted surgery |
title | Ensemble deep learning for the prediction of proficiency at a virtual simulator for robot-assisted surgery |
title_full | Ensemble deep learning for the prediction of proficiency at a virtual simulator for robot-assisted surgery |
title_fullStr | Ensemble deep learning for the prediction of proficiency at a virtual simulator for robot-assisted surgery |
title_full_unstemmed | Ensemble deep learning for the prediction of proficiency at a virtual simulator for robot-assisted surgery |
title_short | Ensemble deep learning for the prediction of proficiency at a virtual simulator for robot-assisted surgery |
title_sort | ensemble deep learning for the prediction of proficiency at a virtual simulator for robot-assisted surgery |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9402513/ https://www.ncbi.nlm.nih.gov/pubmed/35020053 http://dx.doi.org/10.1007/s00464-021-08999-6 |
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