Cargando…
Machine Learning: Using Xception, a Deep Convolutional Neural Network Architecture, to Implement Pectus Excavatum Diagnostic Tool from Frontal-View Chest X-rays
Pectus excavatum (PE), a chest-wall deformity that can compromise cardiopulmonary function, cannot be detected by a radiologist through frontal chest radiography without a lateral view or chest computed tomography. This study aims to train a convolutional neural network (CNN), a deep learning archit...
Autores principales: | , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10045358/ https://www.ncbi.nlm.nih.gov/pubmed/36979738 http://dx.doi.org/10.3390/biomedicines11030760 |
_version_ | 1784913583801368576 |
---|---|
author | Fan, Yu-Jiun Tzeng, I-Shiang Huang, Yao-Sian Hsu, Yuan-Yu Wei, Bo-Chun Hung, Shuo-Ting Cheng, Yeung-Leung |
author_facet | Fan, Yu-Jiun Tzeng, I-Shiang Huang, Yao-Sian Hsu, Yuan-Yu Wei, Bo-Chun Hung, Shuo-Ting Cheng, Yeung-Leung |
author_sort | Fan, Yu-Jiun |
collection | PubMed |
description | Pectus excavatum (PE), a chest-wall deformity that can compromise cardiopulmonary function, cannot be detected by a radiologist through frontal chest radiography without a lateral view or chest computed tomography. This study aims to train a convolutional neural network (CNN), a deep learning architecture with powerful image processing ability, for PE screening through frontal chest radiography, which is the most common imaging test in current hospital practice. Posteroanterior-view chest images of PE and normal patients were collected from our hospital to build the database. Among them, 80% were used as the training set used to train the established CNN algorithm, Xception, whereas the remaining 20% were a test set for model performance evaluation. The performance of our diagnostic artificial intelligence model ranged between 0.976–1 under the receiver operating characteristic curve. The test accuracy of the model reached 0.989, and the sensitivity and specificity were 96.66 and 96.64, respectively. Our study is the first to prove that a CNN can be trained as a diagnostic tool for PE using frontal chest X-rays, which is not possible by the human eye. It offers a convenient way to screen potential candidates for the surgical repair of PE, primarily using available image examinations. |
format | Online Article Text |
id | pubmed-10045358 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-100453582023-03-29 Machine Learning: Using Xception, a Deep Convolutional Neural Network Architecture, to Implement Pectus Excavatum Diagnostic Tool from Frontal-View Chest X-rays Fan, Yu-Jiun Tzeng, I-Shiang Huang, Yao-Sian Hsu, Yuan-Yu Wei, Bo-Chun Hung, Shuo-Ting Cheng, Yeung-Leung Biomedicines Article Pectus excavatum (PE), a chest-wall deformity that can compromise cardiopulmonary function, cannot be detected by a radiologist through frontal chest radiography without a lateral view or chest computed tomography. This study aims to train a convolutional neural network (CNN), a deep learning architecture with powerful image processing ability, for PE screening through frontal chest radiography, which is the most common imaging test in current hospital practice. Posteroanterior-view chest images of PE and normal patients were collected from our hospital to build the database. Among them, 80% were used as the training set used to train the established CNN algorithm, Xception, whereas the remaining 20% were a test set for model performance evaluation. The performance of our diagnostic artificial intelligence model ranged between 0.976–1 under the receiver operating characteristic curve. The test accuracy of the model reached 0.989, and the sensitivity and specificity were 96.66 and 96.64, respectively. Our study is the first to prove that a CNN can be trained as a diagnostic tool for PE using frontal chest X-rays, which is not possible by the human eye. It offers a convenient way to screen potential candidates for the surgical repair of PE, primarily using available image examinations. MDPI 2023-03-02 /pmc/articles/PMC10045358/ /pubmed/36979738 http://dx.doi.org/10.3390/biomedicines11030760 Text en © 2023 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 Fan, Yu-Jiun Tzeng, I-Shiang Huang, Yao-Sian Hsu, Yuan-Yu Wei, Bo-Chun Hung, Shuo-Ting Cheng, Yeung-Leung Machine Learning: Using Xception, a Deep Convolutional Neural Network Architecture, to Implement Pectus Excavatum Diagnostic Tool from Frontal-View Chest X-rays |
title | Machine Learning: Using Xception, a Deep Convolutional Neural Network Architecture, to Implement Pectus Excavatum Diagnostic Tool from Frontal-View Chest X-rays |
title_full | Machine Learning: Using Xception, a Deep Convolutional Neural Network Architecture, to Implement Pectus Excavatum Diagnostic Tool from Frontal-View Chest X-rays |
title_fullStr | Machine Learning: Using Xception, a Deep Convolutional Neural Network Architecture, to Implement Pectus Excavatum Diagnostic Tool from Frontal-View Chest X-rays |
title_full_unstemmed | Machine Learning: Using Xception, a Deep Convolutional Neural Network Architecture, to Implement Pectus Excavatum Diagnostic Tool from Frontal-View Chest X-rays |
title_short | Machine Learning: Using Xception, a Deep Convolutional Neural Network Architecture, to Implement Pectus Excavatum Diagnostic Tool from Frontal-View Chest X-rays |
title_sort | machine learning: using xception, a deep convolutional neural network architecture, to implement pectus excavatum diagnostic tool from frontal-view chest x-rays |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10045358/ https://www.ncbi.nlm.nih.gov/pubmed/36979738 http://dx.doi.org/10.3390/biomedicines11030760 |
work_keys_str_mv | AT fanyujiun machinelearningusingxceptionadeepconvolutionalneuralnetworkarchitecturetoimplementpectusexcavatumdiagnostictoolfromfrontalviewchestxrays AT tzengishiang machinelearningusingxceptionadeepconvolutionalneuralnetworkarchitecturetoimplementpectusexcavatumdiagnostictoolfromfrontalviewchestxrays AT huangyaosian machinelearningusingxceptionadeepconvolutionalneuralnetworkarchitecturetoimplementpectusexcavatumdiagnostictoolfromfrontalviewchestxrays AT hsuyuanyu machinelearningusingxceptionadeepconvolutionalneuralnetworkarchitecturetoimplementpectusexcavatumdiagnostictoolfromfrontalviewchestxrays AT weibochun machinelearningusingxceptionadeepconvolutionalneuralnetworkarchitecturetoimplementpectusexcavatumdiagnostictoolfromfrontalviewchestxrays AT hungshuoting machinelearningusingxceptionadeepconvolutionalneuralnetworkarchitecturetoimplementpectusexcavatumdiagnostictoolfromfrontalviewchestxrays AT chengyeungleung machinelearningusingxceptionadeepconvolutionalneuralnetworkarchitecturetoimplementpectusexcavatumdiagnostictoolfromfrontalviewchestxrays |