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Deep Learning for Automatically Visual Evoked Potential Classification During Surgical Decompression of Sellar Region Tumors
PURPOSE: Detection of the huge amount of data generated in real-time visual evoked potential (VEP) requires labor-intensive work and experienced electrophysiologists. This study aims to build an automatic VEP classification system by using a deep learning algorithm. METHODS: Patients with sellar reg...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Association for Research in Vision and Ophthalmology
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6871542/ https://www.ncbi.nlm.nih.gov/pubmed/31788350 http://dx.doi.org/10.1167/tvst.8.6.21 |
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author | Qiao, Nidan Song, Mengju Ye, Zhao He, Wenqiang Ma, Zengyi Wang, Yongfei Zhang, Yuyan Shou, Xuefei |
author_facet | Qiao, Nidan Song, Mengju Ye, Zhao He, Wenqiang Ma, Zengyi Wang, Yongfei Zhang, Yuyan Shou, Xuefei |
author_sort | Qiao, Nidan |
collection | PubMed |
description | PURPOSE: Detection of the huge amount of data generated in real-time visual evoked potential (VEP) requires labor-intensive work and experienced electrophysiologists. This study aims to build an automatic VEP classification system by using a deep learning algorithm. METHODS: Patients with sellar region tumor and optic chiasm compression were enrolled. Flash VEP monitoring was applied during surgical decompression. Sequential VEP images were fed into three neural network algorithms to train VEP classification models. RESULTS: We included 76 patients. During surgical decompression, we observed 68 eyes with increased VEP amplitude, 47 eyes with a transient decrease, and 37 eyes without change. We generated 2,843 sequences (39,802 images) in total (887 sequences with increasing VEP, 276 sequences with decreasing VEP, and 1680 sequences without change). The model combining convolutional and recurrent neural network had the highest accuracy (87.4%; 95% confidence interval, 84.2%–90.1%). The sensitivity of predicting no change VEP, increasing VEP, and decreasing VEP was 92.6%, 78.9%, and 83.7%, respectively. The specificity of predicting no change VEP, increasing VEP, and decreasing VEP was 80.5%, 93.3%, and 100.0%, respectively. The class activation map visualization technique showed that the P2-N3-P3 complex was important in determining the output. CONCLUSIONS: We identified three VEP responses (no change, increase, and decrease) during transsphenoidal surgical decompression of sellar region tumors. We developed a deep learning model to classify the sequential changes of intraoperative VEP. TRANSLATIONAL RELEVANCE: Our model may have the potential to be applied in real-time monitoring during surgical resection of sellar region tumors. |
format | Online Article Text |
id | pubmed-6871542 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | The Association for Research in Vision and Ophthalmology |
record_format | MEDLINE/PubMed |
spelling | pubmed-68715422019-11-29 Deep Learning for Automatically Visual Evoked Potential Classification During Surgical Decompression of Sellar Region Tumors Qiao, Nidan Song, Mengju Ye, Zhao He, Wenqiang Ma, Zengyi Wang, Yongfei Zhang, Yuyan Shou, Xuefei Transl Vis Sci Technol Articles PURPOSE: Detection of the huge amount of data generated in real-time visual evoked potential (VEP) requires labor-intensive work and experienced electrophysiologists. This study aims to build an automatic VEP classification system by using a deep learning algorithm. METHODS: Patients with sellar region tumor and optic chiasm compression were enrolled. Flash VEP monitoring was applied during surgical decompression. Sequential VEP images were fed into three neural network algorithms to train VEP classification models. RESULTS: We included 76 patients. During surgical decompression, we observed 68 eyes with increased VEP amplitude, 47 eyes with a transient decrease, and 37 eyes without change. We generated 2,843 sequences (39,802 images) in total (887 sequences with increasing VEP, 276 sequences with decreasing VEP, and 1680 sequences without change). The model combining convolutional and recurrent neural network had the highest accuracy (87.4%; 95% confidence interval, 84.2%–90.1%). The sensitivity of predicting no change VEP, increasing VEP, and decreasing VEP was 92.6%, 78.9%, and 83.7%, respectively. The specificity of predicting no change VEP, increasing VEP, and decreasing VEP was 80.5%, 93.3%, and 100.0%, respectively. The class activation map visualization technique showed that the P2-N3-P3 complex was important in determining the output. CONCLUSIONS: We identified three VEP responses (no change, increase, and decrease) during transsphenoidal surgical decompression of sellar region tumors. We developed a deep learning model to classify the sequential changes of intraoperative VEP. TRANSLATIONAL RELEVANCE: Our model may have the potential to be applied in real-time monitoring during surgical resection of sellar region tumors. The Association for Research in Vision and Ophthalmology 2019-11-20 /pmc/articles/PMC6871542/ /pubmed/31788350 http://dx.doi.org/10.1167/tvst.8.6.21 Text en Copyright 2019 The Authors http://creativecommons.org/licenses/by-nc-nd/4.0/ This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. |
spellingShingle | Articles Qiao, Nidan Song, Mengju Ye, Zhao He, Wenqiang Ma, Zengyi Wang, Yongfei Zhang, Yuyan Shou, Xuefei Deep Learning for Automatically Visual Evoked Potential Classification During Surgical Decompression of Sellar Region Tumors |
title | Deep Learning for Automatically Visual Evoked Potential Classification During Surgical Decompression of Sellar Region Tumors |
title_full | Deep Learning for Automatically Visual Evoked Potential Classification During Surgical Decompression of Sellar Region Tumors |
title_fullStr | Deep Learning for Automatically Visual Evoked Potential Classification During Surgical Decompression of Sellar Region Tumors |
title_full_unstemmed | Deep Learning for Automatically Visual Evoked Potential Classification During Surgical Decompression of Sellar Region Tumors |
title_short | Deep Learning for Automatically Visual Evoked Potential Classification During Surgical Decompression of Sellar Region Tumors |
title_sort | deep learning for automatically visual evoked potential classification during surgical decompression of sellar region tumors |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6871542/ https://www.ncbi.nlm.nih.gov/pubmed/31788350 http://dx.doi.org/10.1167/tvst.8.6.21 |
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