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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...

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Autores principales: Shafiei, Somayeh B., Jing, Zhe, Attwood, Kristopher, Iqbal, Umar, Arman, Sena, Hussein, Ahmed A., Durrani, Mohammad, Guru, Khurshid
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
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.
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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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