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Surgical skill level classification model development using EEG and eye-gaze data and machine learning algorithms
The aim of this study was to develop machine learning classification models using electroencephalogram (EEG) and eye-gaze features to predict the level of surgical expertise in robot-assisted surgery (RAS). EEG and eye-gaze data were recorded from 11 participants who performed cystectomy, hysterecto...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer London
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10678814/ https://www.ncbi.nlm.nih.gov/pubmed/37864129 http://dx.doi.org/10.1007/s11701-023-01722-8 |
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author | Shafiei, Somayeh B. Shadpour, Saeed Mohler, James L. Sasangohar, Farzan Gutierrez, Camille Seilanian Toussi, Mehdi Shafqat, Ambreen |
author_facet | Shafiei, Somayeh B. Shadpour, Saeed Mohler, James L. Sasangohar, Farzan Gutierrez, Camille Seilanian Toussi, Mehdi Shafqat, Ambreen |
author_sort | Shafiei, Somayeh B. |
collection | PubMed |
description | The aim of this study was to develop machine learning classification models using electroencephalogram (EEG) and eye-gaze features to predict the level of surgical expertise in robot-assisted surgery (RAS). EEG and eye-gaze data were recorded from 11 participants who performed cystectomy, hysterectomy, and nephrectomy using the da Vinci robot. Skill level was evaluated by an expert RAS surgeon using the modified Global Evaluative Assessment of Robotic Skills (GEARS) tool, and data from three subtasks were extracted to classify skill levels using three classification models—multinomial logistic regression (MLR), random forest (RF), and gradient boosting (GB). The GB algorithm was used with a combination of EEG and eye-gaze data to classify skill levels, and differences between the models were tested using two-sample t tests. The GB model using EEG features showed the best performance for blunt dissection (83% accuracy), retraction (85% accuracy), and burn dissection (81% accuracy). The combination of EEG and eye-gaze features using the GB algorithm improved the accuracy of skill level classification to 88% for blunt dissection, 93% for retraction, and 86% for burn dissection. The implementation of objective skill classification models in clinical settings may enhance the RAS surgical training process by providing objective feedback about performance to surgeons and their teachers. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11701-023-01722-8. |
format | Online Article Text |
id | pubmed-10678814 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer London |
record_format | MEDLINE/PubMed |
spelling | pubmed-106788142023-10-21 Surgical skill level classification model development using EEG and eye-gaze data and machine learning algorithms Shafiei, Somayeh B. Shadpour, Saeed Mohler, James L. Sasangohar, Farzan Gutierrez, Camille Seilanian Toussi, Mehdi Shafqat, Ambreen J Robot Surg Research The aim of this study was to develop machine learning classification models using electroencephalogram (EEG) and eye-gaze features to predict the level of surgical expertise in robot-assisted surgery (RAS). EEG and eye-gaze data were recorded from 11 participants who performed cystectomy, hysterectomy, and nephrectomy using the da Vinci robot. Skill level was evaluated by an expert RAS surgeon using the modified Global Evaluative Assessment of Robotic Skills (GEARS) tool, and data from three subtasks were extracted to classify skill levels using three classification models—multinomial logistic regression (MLR), random forest (RF), and gradient boosting (GB). The GB algorithm was used with a combination of EEG and eye-gaze data to classify skill levels, and differences between the models were tested using two-sample t tests. The GB model using EEG features showed the best performance for blunt dissection (83% accuracy), retraction (85% accuracy), and burn dissection (81% accuracy). The combination of EEG and eye-gaze features using the GB algorithm improved the accuracy of skill level classification to 88% for blunt dissection, 93% for retraction, and 86% for burn dissection. The implementation of objective skill classification models in clinical settings may enhance the RAS surgical training process by providing objective feedback about performance to surgeons and their teachers. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11701-023-01722-8. Springer London 2023-10-21 2023 /pmc/articles/PMC10678814/ /pubmed/37864129 http://dx.doi.org/10.1007/s11701-023-01722-8 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This 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 | Research Shafiei, Somayeh B. Shadpour, Saeed Mohler, James L. Sasangohar, Farzan Gutierrez, Camille Seilanian Toussi, Mehdi Shafqat, Ambreen Surgical skill level classification model development using EEG and eye-gaze data and machine learning algorithms |
title | Surgical skill level classification model development using EEG and eye-gaze data and machine learning algorithms |
title_full | Surgical skill level classification model development using EEG and eye-gaze data and machine learning algorithms |
title_fullStr | Surgical skill level classification model development using EEG and eye-gaze data and machine learning algorithms |
title_full_unstemmed | Surgical skill level classification model development using EEG and eye-gaze data and machine learning algorithms |
title_short | Surgical skill level classification model development using EEG and eye-gaze data and machine learning algorithms |
title_sort | surgical skill level classification model development using eeg and eye-gaze data and machine learning algorithms |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10678814/ https://www.ncbi.nlm.nih.gov/pubmed/37864129 http://dx.doi.org/10.1007/s11701-023-01722-8 |
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