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Applying a deep convolutional neural network to monitor the lateral spread response during microvascular surgery for hemifacial spasm
BACKGROUND: Intraoperative neurophysiological monitoring is essential in neurosurgical procedures. In this study, we built and evaluated the performance of a deep neural network in differentiating between the presence and absence of a lateral spread response, which provides critical information duri...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9629649/ https://www.ncbi.nlm.nih.gov/pubmed/36322573 http://dx.doi.org/10.1371/journal.pone.0276378 |
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author | Kim, Minsoo Park, Sang-Ku Kubota, Yasuhiro Lee, Seunghoon Park, Kwan Kong, Doo-Sik |
author_facet | Kim, Minsoo Park, Sang-Ku Kubota, Yasuhiro Lee, Seunghoon Park, Kwan Kong, Doo-Sik |
author_sort | Kim, Minsoo |
collection | PubMed |
description | BACKGROUND: Intraoperative neurophysiological monitoring is essential in neurosurgical procedures. In this study, we built and evaluated the performance of a deep neural network in differentiating between the presence and absence of a lateral spread response, which provides critical information during microvascular decompression surgery for the treatment of hemifacial spasm using intraoperatively acquired electromyography images. METHODS AND FINDINGS: A total of 3,674 image screenshots of monitoring devices from 50 patients were prepared, preprocessed, and then adopted into training and validation sets. A deep neural network was constructed using current-standard, off-the-shelf tools. The neural network correctly differentiated 50 test images (accuracy, 100%; area under the curve, 0.96) collected from 25 patients whose data were never exposed to the neural network during training or validation. The accuracy of the network was equivalent to that of the neuromonitoring technologists (p = 0.3013) and higher than that of neurosurgeons experienced in hemifacial spasm (p < 0.0001). Heatmaps obtained to highlight the key region of interest achieved a level similar to that of trained human professionals. Provisional clinical application showed that the neural network was preferable as an auxiliary tool. CONCLUSIONS: A deep neural network trained on a dataset of intraoperatively collected electromyography data could classify the presence and absence of the lateral spread response with equivalent performance to human professionals. Well-designated applications based upon the neural network may provide useful auxiliary tools for surgical teams during operations. |
format | Online Article Text |
id | pubmed-9629649 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-96296492022-11-03 Applying a deep convolutional neural network to monitor the lateral spread response during microvascular surgery for hemifacial spasm Kim, Minsoo Park, Sang-Ku Kubota, Yasuhiro Lee, Seunghoon Park, Kwan Kong, Doo-Sik PLoS One Research Article BACKGROUND: Intraoperative neurophysiological monitoring is essential in neurosurgical procedures. In this study, we built and evaluated the performance of a deep neural network in differentiating between the presence and absence of a lateral spread response, which provides critical information during microvascular decompression surgery for the treatment of hemifacial spasm using intraoperatively acquired electromyography images. METHODS AND FINDINGS: A total of 3,674 image screenshots of monitoring devices from 50 patients were prepared, preprocessed, and then adopted into training and validation sets. A deep neural network was constructed using current-standard, off-the-shelf tools. The neural network correctly differentiated 50 test images (accuracy, 100%; area under the curve, 0.96) collected from 25 patients whose data were never exposed to the neural network during training or validation. The accuracy of the network was equivalent to that of the neuromonitoring technologists (p = 0.3013) and higher than that of neurosurgeons experienced in hemifacial spasm (p < 0.0001). Heatmaps obtained to highlight the key region of interest achieved a level similar to that of trained human professionals. Provisional clinical application showed that the neural network was preferable as an auxiliary tool. CONCLUSIONS: A deep neural network trained on a dataset of intraoperatively collected electromyography data could classify the presence and absence of the lateral spread response with equivalent performance to human professionals. Well-designated applications based upon the neural network may provide useful auxiliary tools for surgical teams during operations. Public Library of Science 2022-11-02 /pmc/articles/PMC9629649/ /pubmed/36322573 http://dx.doi.org/10.1371/journal.pone.0276378 Text en © 2022 Kim et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Kim, Minsoo Park, Sang-Ku Kubota, Yasuhiro Lee, Seunghoon Park, Kwan Kong, Doo-Sik Applying a deep convolutional neural network to monitor the lateral spread response during microvascular surgery for hemifacial spasm |
title | Applying a deep convolutional neural network to monitor the lateral spread response during microvascular surgery for hemifacial spasm |
title_full | Applying a deep convolutional neural network to monitor the lateral spread response during microvascular surgery for hemifacial spasm |
title_fullStr | Applying a deep convolutional neural network to monitor the lateral spread response during microvascular surgery for hemifacial spasm |
title_full_unstemmed | Applying a deep convolutional neural network to monitor the lateral spread response during microvascular surgery for hemifacial spasm |
title_short | Applying a deep convolutional neural network to monitor the lateral spread response during microvascular surgery for hemifacial spasm |
title_sort | applying a deep convolutional neural network to monitor the lateral spread response during microvascular surgery for hemifacial spasm |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9629649/ https://www.ncbi.nlm.nih.gov/pubmed/36322573 http://dx.doi.org/10.1371/journal.pone.0276378 |
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