Prediction of osteoporosis from simple hip radiography using deep learning algorithm

Abstract Despite being the gold standard for diagnosis of osteoporosis, dual-energy X-ray absorptiometry (DXA) could not be widely used as a screening tool for osteoporosis. This study aimed to predict osteoporosis via simple hip radiography using deep learning algorithm. A total of 1001 datasets of...

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Autores principales:	Ryoungwoo Jang, Jae Ho Choi, Namkug Kim, Jae Suk Chang, Pil Whan Yoon, Chul-Ho Kim
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/2f106b5b9b5d49a1809e7138a2bf9153
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spelling	oai:doaj.org-article:2f106b5b9b5d49a1809e7138a2bf91532021-12-02T17:13:17ZPrediction of osteoporosis from simple hip radiography using deep learning algorithm10.1038/s41598-021-99549-62045-2322https://doaj.org/article/2f106b5b9b5d49a1809e7138a2bf91532021-10-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-99549-6https://doaj.org/toc/2045-2322Abstract Despite being the gold standard for diagnosis of osteoporosis, dual-energy X-ray absorptiometry (DXA) could not be widely used as a screening tool for osteoporosis. This study aimed to predict osteoporosis via simple hip radiography using deep learning algorithm. A total of 1001 datasets of proximal femur DXA with matched same-side cropped simple hip bone radiographic images of female patients aged ≥ 55 years were collected. Of these, 504 patients had osteoporosis (T-score ≤ − 2.5), and 497 patients did not have osteoporosis. The 1001 images were randomly divided into three sets: 800 images for the training, 100 images for the validation, and 101 images for the test. Based on VGG16 equipped with nonlocal neural network, we developed a deep neural network (DNN) model. We calculated the confusion matrix and evaluated the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). We drew the receiver operating characteristic (ROC) curve. A gradient-based class activation map (Grad-CAM) overlapping the original image was also used to visualize the model performance. Additionally, we performed external validation using 117 datasets. Our final DNN model showed an overall accuracy of 81.2%, sensitivity of 91.1%, and specificity of 68.9%. The PPV was 78.5%, and the NPV was 86.1%. The area under the ROC curve value was 0.867, indicating a reasonable performance for screening osteoporosis by simple hip radiography. The external validation set confirmed a model performance with an overall accuracy of 71.8% and an AUC value of 0.700. All Grad-CAM results from both internal and external validation sets appropriately matched the proximal femur cortex and trabecular patterns of the radiographs. The DNN model could be considered as one of the useful screening tools for easy prediction of osteoporosis in the real-world clinical setting.Ryoungwoo JangJae Ho ChoiNamkug KimJae Suk ChangPil Whan YoonChul-Ho KimNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ryoungwoo Jang Jae Ho Choi Namkug Kim Jae Suk Chang Pil Whan Yoon Chul-Ho Kim Prediction of osteoporosis from simple hip radiography using deep learning algorithm
description	Abstract Despite being the gold standard for diagnosis of osteoporosis, dual-energy X-ray absorptiometry (DXA) could not be widely used as a screening tool for osteoporosis. This study aimed to predict osteoporosis via simple hip radiography using deep learning algorithm. A total of 1001 datasets of proximal femur DXA with matched same-side cropped simple hip bone radiographic images of female patients aged ≥ 55 years were collected. Of these, 504 patients had osteoporosis (T-score ≤ − 2.5), and 497 patients did not have osteoporosis. The 1001 images were randomly divided into three sets: 800 images for the training, 100 images for the validation, and 101 images for the test. Based on VGG16 equipped with nonlocal neural network, we developed a deep neural network (DNN) model. We calculated the confusion matrix and evaluated the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). We drew the receiver operating characteristic (ROC) curve. A gradient-based class activation map (Grad-CAM) overlapping the original image was also used to visualize the model performance. Additionally, we performed external validation using 117 datasets. Our final DNN model showed an overall accuracy of 81.2%, sensitivity of 91.1%, and specificity of 68.9%. The PPV was 78.5%, and the NPV was 86.1%. The area under the ROC curve value was 0.867, indicating a reasonable performance for screening osteoporosis by simple hip radiography. The external validation set confirmed a model performance with an overall accuracy of 71.8% and an AUC value of 0.700. All Grad-CAM results from both internal and external validation sets appropriately matched the proximal femur cortex and trabecular patterns of the radiographs. The DNN model could be considered as one of the useful screening tools for easy prediction of osteoporosis in the real-world clinical setting.
format	article
author	Ryoungwoo Jang Jae Ho Choi Namkug Kim Jae Suk Chang Pil Whan Yoon Chul-Ho Kim
author_facet	Ryoungwoo Jang Jae Ho Choi Namkug Kim Jae Suk Chang Pil Whan Yoon Chul-Ho Kim
author_sort	Ryoungwoo Jang
title	Prediction of osteoporosis from simple hip radiography using deep learning algorithm
title_short	Prediction of osteoporosis from simple hip radiography using deep learning algorithm
title_full	Prediction of osteoporosis from simple hip radiography using deep learning algorithm
title_fullStr	Prediction of osteoporosis from simple hip radiography using deep learning algorithm
title_full_unstemmed	Prediction of osteoporosis from simple hip radiography using deep learning algorithm
title_sort	prediction of osteoporosis from simple hip radiography using deep learning algorithm
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/2f106b5b9b5d49a1809e7138a2bf9153
work_keys_str_mv	AT ryoungwoojang predictionofosteoporosisfromsimplehipradiographyusingdeeplearningalgorithm AT jaehochoi predictionofosteoporosisfromsimplehipradiographyusingdeeplearningalgorithm AT namkugkim predictionofosteoporosisfromsimplehipradiographyusingdeeplearningalgorithm AT jaesukchang predictionofosteoporosisfromsimplehipradiographyusingdeeplearningalgorithm AT pilwhanyoon predictionofosteoporosisfromsimplehipradiographyusingdeeplearningalgorithm AT chulhokim predictionofosteoporosisfromsimplehipradiographyusingdeeplearningalgorithm
_version_	1718381368573952000

Prediction of osteoporosis from simple hip radiography using deep learning algorithm

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