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Association between Functional Brain Network Metrics and Surgeon Performance and Distraction in the Operating Room
Objective: The aim of this work was to examine (electroencephalogram) EEG features that represent dynamic changes in the functional brain network of a surgical trainee and whether these features can be used to evaluate a robot assisted surgeon’s (RAS) performance and distraction level in the operati...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8068138/ https://www.ncbi.nlm.nih.gov/pubmed/33917719 http://dx.doi.org/10.3390/brainsci11040468 |
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author | Shafiei, Somayeh B. Jing, Zhe Attwood, Kristopher Iqbal, Umar Arman, Sena Hussein, Ahmed A. Durrani, Mohammad Guru, Khurshid |
author_facet | Shafiei, Somayeh B. Jing, Zhe Attwood, Kristopher Iqbal, Umar Arman, Sena Hussein, Ahmed A. Durrani, Mohammad Guru, Khurshid |
author_sort | Shafiei, Somayeh B. |
collection | PubMed |
description | Objective: The aim of this work was to examine (electroencephalogram) EEG features that represent dynamic changes in the functional brain network of a surgical trainee and whether these features can be used to evaluate a robot assisted surgeon’s (RAS) performance and distraction level in the operating room. Materials and Methods: Electroencephalogram (EEG) data were collected from three robotic surgeons in an operating room (OR) via a 128-channel EEG headset with a frequency of 500 samples/second. Signal processing and network neuroscience algorithms were applied to the data to extract EEG features. The SURG-TLX and NASA-TLX metrics were subjectively evaluated by a surgeon and mentor at the end of each task. The scores given to performance and distraction metrics were used in the analyses here. Statistical test data were utilized to select EEG features that have a significant relationship with surgeon performance and distraction while carrying out a RAS surgical task in the OR. Results: RAS surgeon performance and distraction had a relationship with the surgeon’s functional brain network metrics as recorded throughout OR surgery. We also found a significant negative Pearson correlation between performance and the distraction level (−0.37, p-value < 0.0001). Conclusions: The method proposed in this study has potential for evaluating RAS surgeon performance and the level of distraction. This has possible applications in improving patient safety, surgical mentorship, and training. |
format | Online Article Text |
id | pubmed-8068138 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-80681382021-04-25 Association between Functional Brain Network Metrics and Surgeon Performance and Distraction in the Operating Room Shafiei, Somayeh B. Jing, Zhe Attwood, Kristopher Iqbal, Umar Arman, Sena Hussein, Ahmed A. Durrani, Mohammad Guru, Khurshid Brain Sci Article Objective: The aim of this work was to examine (electroencephalogram) EEG features that represent dynamic changes in the functional brain network of a surgical trainee and whether these features can be used to evaluate a robot assisted surgeon’s (RAS) performance and distraction level in the operating room. Materials and Methods: Electroencephalogram (EEG) data were collected from three robotic surgeons in an operating room (OR) via a 128-channel EEG headset with a frequency of 500 samples/second. Signal processing and network neuroscience algorithms were applied to the data to extract EEG features. The SURG-TLX and NASA-TLX metrics were subjectively evaluated by a surgeon and mentor at the end of each task. The scores given to performance and distraction metrics were used in the analyses here. Statistical test data were utilized to select EEG features that have a significant relationship with surgeon performance and distraction while carrying out a RAS surgical task in the OR. Results: RAS surgeon performance and distraction had a relationship with the surgeon’s functional brain network metrics as recorded throughout OR surgery. We also found a significant negative Pearson correlation between performance and the distraction level (−0.37, p-value < 0.0001). Conclusions: The method proposed in this study has potential for evaluating RAS surgeon performance and the level of distraction. This has possible applications in improving patient safety, surgical mentorship, and training. MDPI 2021-04-08 /pmc/articles/PMC8068138/ /pubmed/33917719 http://dx.doi.org/10.3390/brainsci11040468 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Shafiei, Somayeh B. Jing, Zhe Attwood, Kristopher Iqbal, Umar Arman, Sena Hussein, Ahmed A. Durrani, Mohammad Guru, Khurshid Association between Functional Brain Network Metrics and Surgeon Performance and Distraction in the Operating Room |
title | Association between Functional Brain Network Metrics and Surgeon Performance and Distraction in the Operating Room |
title_full | Association between Functional Brain Network Metrics and Surgeon Performance and Distraction in the Operating Room |
title_fullStr | Association between Functional Brain Network Metrics and Surgeon Performance and Distraction in the Operating Room |
title_full_unstemmed | Association between Functional Brain Network Metrics and Surgeon Performance and Distraction in the Operating Room |
title_short | Association between Functional Brain Network Metrics and Surgeon Performance and Distraction in the Operating Room |
title_sort | association between functional brain network metrics and surgeon performance and distraction in the operating room |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8068138/ https://www.ncbi.nlm.nih.gov/pubmed/33917719 http://dx.doi.org/10.3390/brainsci11040468 |
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