Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Kim, Minsoo, Park, Sang-Ku, Kubota, Yasuhiro, Lee, Seunghoon, Park, Kwan, Kong, Doo-Sik
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
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
_version_ 1784823444172439552
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
work_keys_str_mv AT kimminsoo applyingadeepconvolutionalneuralnetworktomonitorthelateralspreadresponseduringmicrovascularsurgeryforhemifacialspasm
AT parksangku applyingadeepconvolutionalneuralnetworktomonitorthelateralspreadresponseduringmicrovascularsurgeryforhemifacialspasm
AT kubotayasuhiro applyingadeepconvolutionalneuralnetworktomonitorthelateralspreadresponseduringmicrovascularsurgeryforhemifacialspasm
AT leeseunghoon applyingadeepconvolutionalneuralnetworktomonitorthelateralspreadresponseduringmicrovascularsurgeryforhemifacialspasm
AT parkkwan applyingadeepconvolutionalneuralnetworktomonitorthelateralspreadresponseduringmicrovascularsurgeryforhemifacialspasm
AT kongdoosik applyingadeepconvolutionalneuralnetworktomonitorthelateralspreadresponseduringmicrovascularsurgeryforhemifacialspasm