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Prediction of Hearing Prognosis of Large Vestibular Aqueduct Syndrome Based on the PyTorch Deep Learning Model

In order to compare magnetic resonance imaging (MRI) findings of patients with large vestibular aqueduct syndrome (LVAS) in the stable hearing loss (HL) group and the fluctuating HL group, this paper provides reference for clinicians' early intervention. From January 2001 to January 2016, patie...

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Autores principales: Duan, Bo, Xu, Zhengmin, Pan, Lili, Chen, Wenxia, Qiao, Zhongwei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9020928/
https://www.ncbi.nlm.nih.gov/pubmed/35463685
http://dx.doi.org/10.1155/2022/4814577
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author Duan, Bo
Xu, Zhengmin
Pan, Lili
Chen, Wenxia
Qiao, Zhongwei
author_facet Duan, Bo
Xu, Zhengmin
Pan, Lili
Chen, Wenxia
Qiao, Zhongwei
author_sort Duan, Bo
collection PubMed
description In order to compare magnetic resonance imaging (MRI) findings of patients with large vestibular aqueduct syndrome (LVAS) in the stable hearing loss (HL) group and the fluctuating HL group, this paper provides reference for clinicians' early intervention. From January 2001 to January 2016, patients with hearing impairment diagnosed as LVAS in infancy in the Department of Otorhinolaryngology, Head and Neck Surgery, Children's Hospital of Fudan University were collected and divided into the stable HL group (n = 29) and the fluctuating HL group (n = 30). MRI images at initial diagnosis were collected, and various deep learning neural network training models were established based on PyTorch to classify and predict the two series. Vgg16_bn, vgg19_bn, and ResNet18, convolutional neural networks (CNNs) with fewer layers, had favorable effects for model building, with accs of 0.9, 0.8, and 0.85, respectively. ResNet50, a CNN with multiple layers and an acc of 0.54, had relatively poor effects. The GoogLeNet-trained model performed best, with an acc of 0.98. We conclude that deep learning-based radiomics can assist doctors in accurately predicting LVAS patients to classify them into either fluctuating or stable HL types and adopt differentiated treatment methods.
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spelling pubmed-90209282022-04-21 Prediction of Hearing Prognosis of Large Vestibular Aqueduct Syndrome Based on the PyTorch Deep Learning Model Duan, Bo Xu, Zhengmin Pan, Lili Chen, Wenxia Qiao, Zhongwei J Healthc Eng Research Article In order to compare magnetic resonance imaging (MRI) findings of patients with large vestibular aqueduct syndrome (LVAS) in the stable hearing loss (HL) group and the fluctuating HL group, this paper provides reference for clinicians' early intervention. From January 2001 to January 2016, patients with hearing impairment diagnosed as LVAS in infancy in the Department of Otorhinolaryngology, Head and Neck Surgery, Children's Hospital of Fudan University were collected and divided into the stable HL group (n = 29) and the fluctuating HL group (n = 30). MRI images at initial diagnosis were collected, and various deep learning neural network training models were established based on PyTorch to classify and predict the two series. Vgg16_bn, vgg19_bn, and ResNet18, convolutional neural networks (CNNs) with fewer layers, had favorable effects for model building, with accs of 0.9, 0.8, and 0.85, respectively. ResNet50, a CNN with multiple layers and an acc of 0.54, had relatively poor effects. The GoogLeNet-trained model performed best, with an acc of 0.98. We conclude that deep learning-based radiomics can assist doctors in accurately predicting LVAS patients to classify them into either fluctuating or stable HL types and adopt differentiated treatment methods. Hindawi 2022-04-13 /pmc/articles/PMC9020928/ /pubmed/35463685 http://dx.doi.org/10.1155/2022/4814577 Text en Copyright © 2022 Bo Duan et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Duan, Bo
Xu, Zhengmin
Pan, Lili
Chen, Wenxia
Qiao, Zhongwei
Prediction of Hearing Prognosis of Large Vestibular Aqueduct Syndrome Based on the PyTorch Deep Learning Model
title Prediction of Hearing Prognosis of Large Vestibular Aqueduct Syndrome Based on the PyTorch Deep Learning Model
title_full Prediction of Hearing Prognosis of Large Vestibular Aqueduct Syndrome Based on the PyTorch Deep Learning Model
title_fullStr Prediction of Hearing Prognosis of Large Vestibular Aqueduct Syndrome Based on the PyTorch Deep Learning Model
title_full_unstemmed Prediction of Hearing Prognosis of Large Vestibular Aqueduct Syndrome Based on the PyTorch Deep Learning Model
title_short Prediction of Hearing Prognosis of Large Vestibular Aqueduct Syndrome Based on the PyTorch Deep Learning Model
title_sort prediction of hearing prognosis of large vestibular aqueduct syndrome based on the pytorch deep learning model
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9020928/
https://www.ncbi.nlm.nih.gov/pubmed/35463685
http://dx.doi.org/10.1155/2022/4814577
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