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Deep Learning Algorithms with Demographic Information Help to Detect Tuberculosis in Chest Radiographs in Annual Workers’ Health Examination Data

We aimed to use deep learning to detect tuberculosis in chest radiographs in annual workers’ health examination data and compare the performances of convolutional neural networks (CNNs) based on images only (I-CNN) and CNNs including demographic variables (D-CNN). The I-CNN and D-CNN models were tra...

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Autores principales: Heo, Seok-Jae, Kim, Yangwook, Yun, Sehyun, Lim, Sung-Shil, Kim, Jihyun, Nam, Chung-Mo, Park, Eun-Cheol, Jung, Inkyung, Yoon, Jin-Ha
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6352082/
https://www.ncbi.nlm.nih.gov/pubmed/30654560
http://dx.doi.org/10.3390/ijerph16020250
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author Heo, Seok-Jae
Kim, Yangwook
Yun, Sehyun
Lim, Sung-Shil
Kim, Jihyun
Nam, Chung-Mo
Park, Eun-Cheol
Jung, Inkyung
Yoon, Jin-Ha
author_facet Heo, Seok-Jae
Kim, Yangwook
Yun, Sehyun
Lim, Sung-Shil
Kim, Jihyun
Nam, Chung-Mo
Park, Eun-Cheol
Jung, Inkyung
Yoon, Jin-Ha
author_sort Heo, Seok-Jae
collection PubMed
description We aimed to use deep learning to detect tuberculosis in chest radiographs in annual workers’ health examination data and compare the performances of convolutional neural networks (CNNs) based on images only (I-CNN) and CNNs including demographic variables (D-CNN). The I-CNN and D-CNN models were trained on 1000 chest X-ray images, both positive and negative, for tuberculosis. Feature extraction was conducted using VGG19, InceptionV3, ResNet50, DenseNet121, and InceptionResNetV2. Age, weight, height, and gender were recorded as demographic variables. The area under the receiver operating characteristic (ROC) curve (AUC) was calculated for model comparison. The AUC values of the D-CNN models were greater than that of I-CNN. The AUC values for VGG19 increased by 0.0144 (0.957 to 0.9714) in the training set, and by 0.0138 (0.9075 to 0.9213) in the test set (both p < 0.05). The D-CNN models show greater sensitivity than I-CNN models (0.815 vs. 0.775, respectively) at the same cut-off point for the same specificity of 0.962. The sensitivity of D-CNN does not attenuate as much as that of I-CNN, even when specificity is increased by cut-off points. Conclusion: Our results indicate that machine learning can facilitate the detection of tuberculosis in chest X-rays, and demographic factors can improve this process.
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spelling pubmed-63520822019-02-01 Deep Learning Algorithms with Demographic Information Help to Detect Tuberculosis in Chest Radiographs in Annual Workers’ Health Examination Data Heo, Seok-Jae Kim, Yangwook Yun, Sehyun Lim, Sung-Shil Kim, Jihyun Nam, Chung-Mo Park, Eun-Cheol Jung, Inkyung Yoon, Jin-Ha Int J Environ Res Public Health Article We aimed to use deep learning to detect tuberculosis in chest radiographs in annual workers’ health examination data and compare the performances of convolutional neural networks (CNNs) based on images only (I-CNN) and CNNs including demographic variables (D-CNN). The I-CNN and D-CNN models were trained on 1000 chest X-ray images, both positive and negative, for tuberculosis. Feature extraction was conducted using VGG19, InceptionV3, ResNet50, DenseNet121, and InceptionResNetV2. Age, weight, height, and gender were recorded as demographic variables. The area under the receiver operating characteristic (ROC) curve (AUC) was calculated for model comparison. The AUC values of the D-CNN models were greater than that of I-CNN. The AUC values for VGG19 increased by 0.0144 (0.957 to 0.9714) in the training set, and by 0.0138 (0.9075 to 0.9213) in the test set (both p < 0.05). The D-CNN models show greater sensitivity than I-CNN models (0.815 vs. 0.775, respectively) at the same cut-off point for the same specificity of 0.962. The sensitivity of D-CNN does not attenuate as much as that of I-CNN, even when specificity is increased by cut-off points. Conclusion: Our results indicate that machine learning can facilitate the detection of tuberculosis in chest X-rays, and demographic factors can improve this process. MDPI 2019-01-16 2019-01 /pmc/articles/PMC6352082/ /pubmed/30654560 http://dx.doi.org/10.3390/ijerph16020250 Text en © 2019 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Heo, Seok-Jae
Kim, Yangwook
Yun, Sehyun
Lim, Sung-Shil
Kim, Jihyun
Nam, Chung-Mo
Park, Eun-Cheol
Jung, Inkyung
Yoon, Jin-Ha
Deep Learning Algorithms with Demographic Information Help to Detect Tuberculosis in Chest Radiographs in Annual Workers’ Health Examination Data
title Deep Learning Algorithms with Demographic Information Help to Detect Tuberculosis in Chest Radiographs in Annual Workers’ Health Examination Data
title_full Deep Learning Algorithms with Demographic Information Help to Detect Tuberculosis in Chest Radiographs in Annual Workers’ Health Examination Data
title_fullStr Deep Learning Algorithms with Demographic Information Help to Detect Tuberculosis in Chest Radiographs in Annual Workers’ Health Examination Data
title_full_unstemmed Deep Learning Algorithms with Demographic Information Help to Detect Tuberculosis in Chest Radiographs in Annual Workers’ Health Examination Data
title_short Deep Learning Algorithms with Demographic Information Help to Detect Tuberculosis in Chest Radiographs in Annual Workers’ Health Examination Data
title_sort deep learning algorithms with demographic information help to detect tuberculosis in chest radiographs in annual workers’ health examination data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6352082/
https://www.ncbi.nlm.nih.gov/pubmed/30654560
http://dx.doi.org/10.3390/ijerph16020250
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