Opportunistic Screening of Osteoporosis Using Plain Film Chest X-Ray

Fakai Wang; Kang Zheng; Yirui Wang; Xiaoyun Zhou; Le Lu; Jing Xiao; Min Wu; Chang Fu Kuo; Shun Miao

doi:10.1007/978-3-030-87602-9_13

Opportunistic Screening of Osteoporosis Using Plain Film Chest X-Ray

Fakai Wang^*, Kang Zheng, Yirui Wang, Xiaoyun Zhou, Le Lu, Jing Xiao, Min Wu, Chang Fu Kuo, Shun Miao

^*Corresponding author for this work

School of Medicine

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Scopus citations

Abstract

Osteoporosis is a common chronic metabolic bone disease that is often under-diagnosed and under-treated due to the limited access to bone mineral density (BMD) examinations, e.g., Dual-energy X-ray Absorptiometry (DXA). In this paper, we propose a method to predict BMD from Chest X-ray (CXR), one of the most common, accessible and low-cost medical image examinations. Our method first automatically detects Regions of Interest (ROIs) of local and global bone structures from the CXR. Then a multi-ROI model is developed to exploit both local and global information in the chest X-ray image for accurate BMD estimation. Our method is evaluated on 1651 CXR cases with ground truth BMD measured by gold standard DXA. The model predicted BMD has a strong correlation with the ground truth (Pearson correlation coefficient 0.853). When applied for osteoporosis screening, it achieves a high classification performance (AUC 0.928). As the first effort in the field using CXR scans to predict the BMD, the proposed algorithm holds a strong potential in early osteoporosis screening and public health promotion.

Original language	English
Title of host publication	Predictive Intelligence in Medicine - 4th International Workshop, PRIME 2021, Held in Conjunction with MICCAI 2021, Proceedings
Editors	Islem Rekik, Ehsan Adeli, Sang Hyun Park, Julia Schnabel
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	138-146
Number of pages	9
ISBN (Print)	9783030876012
DOIs	https://doi.org/10.1007/978-3-030-87602-9_13
State	Published - 2021
Event	4th International Workshop on Predictive Intelligence in Medicine, PRIME 2021, held in conjunction with 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021 - Virtual, Online Duration: 01 10 2021 → 01 10 2021

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12928 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	4th International Workshop on Predictive Intelligence in Medicine, PRIME 2021, held in conjunction with 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021
City	Virtual, Online
Period	01/10/21 → 01/10/21

Bibliographical note

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

Keywords

Bone mineral density estimation
Chest X-ray
Multi-ROI model

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-030-87602-9_13

Cite this

Wang, F., Zheng, K., Wang, Y., Zhou, X., Lu, L., Xiao, J., Wu, M., Kuo, C. F., & Miao, S. (2021). Opportunistic Screening of Osteoporosis Using Plain Film Chest X-Ray. In I. Rekik, E. Adeli, S. H. Park, & J. Schnabel (Eds.), Predictive Intelligence in Medicine - 4th International Workshop, PRIME 2021, Held in Conjunction with MICCAI 2021, Proceedings (pp. 138-146). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12928 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-87602-9_13

Wang, Fakai ; Zheng, Kang ; Wang, Yirui et al. / Opportunistic Screening of Osteoporosis Using Plain Film Chest X-Ray. Predictive Intelligence in Medicine - 4th International Workshop, PRIME 2021, Held in Conjunction with MICCAI 2021, Proceedings. editor / Islem Rekik ; Ehsan Adeli ; Sang Hyun Park ; Julia Schnabel. Springer Science and Business Media Deutschland GmbH, 2021. pp. 138-146 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{b103512cc3a84da19e09ff8f4a034757,

title = "Opportunistic Screening of Osteoporosis Using Plain Film Chest X-Ray",

abstract = "Osteoporosis is a common chronic metabolic bone disease that is often under-diagnosed and under-treated due to the limited access to bone mineral density (BMD) examinations, e.g., Dual-energy X-ray Absorptiometry (DXA). In this paper, we propose a method to predict BMD from Chest X-ray (CXR), one of the most common, accessible and low-cost medical image examinations. Our method first automatically detects Regions of Interest (ROIs) of local and global bone structures from the CXR. Then a multi-ROI model is developed to exploit both local and global information in the chest X-ray image for accurate BMD estimation. Our method is evaluated on 1651 CXR cases with ground truth BMD measured by gold standard DXA. The model predicted BMD has a strong correlation with the ground truth (Pearson correlation coefficient 0.853). When applied for osteoporosis screening, it achieves a high classification performance (AUC 0.928). As the first effort in the field using CXR scans to predict the BMD, the proposed algorithm holds a strong potential in early osteoporosis screening and public health promotion.",

keywords = "Bone mineral density estimation, Chest X-ray, Multi-ROI model",

author = "Fakai Wang and Kang Zheng and Yirui Wang and Xiaoyun Zhou and Le Lu and Jing Xiao and Min Wu and Kuo, {Chang Fu} and Shun Miao",

note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 4th International Workshop on Predictive Intelligence in Medicine, PRIME 2021, held in conjunction with 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021 ; Conference date: 01-10-2021 Through 01-10-2021",

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doi = "10.1007/978-3-030-87602-9_13",

language = "英语",

isbn = "9783030876012",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "138--146",

editor = "Islem Rekik and Ehsan Adeli and Park, {Sang Hyun} and Julia Schnabel",

booktitle = "Predictive Intelligence in Medicine - 4th International Workshop, PRIME 2021, Held in Conjunction with MICCAI 2021, Proceedings",

address = "德国",

}

Wang, F, Zheng, K, Wang, Y, Zhou, X, Lu, L, Xiao, J, Wu, M, Kuo, CF & Miao, S 2021, Opportunistic Screening of Osteoporosis Using Plain Film Chest X-Ray. in I Rekik, E Adeli, SH Park & J Schnabel (eds), Predictive Intelligence in Medicine - 4th International Workshop, PRIME 2021, Held in Conjunction with MICCAI 2021, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12928 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 138-146, 4th International Workshop on Predictive Intelligence in Medicine, PRIME 2021, held in conjunction with 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021, Virtual, Online, 01/10/21. https://doi.org/10.1007/978-3-030-87602-9_13

Opportunistic Screening of Osteoporosis Using Plain Film Chest X-Ray. / Wang, Fakai; Zheng, Kang; Wang, Yirui et al.
Predictive Intelligence in Medicine - 4th International Workshop, PRIME 2021, Held in Conjunction with MICCAI 2021, Proceedings. ed. / Islem Rekik; Ehsan Adeli; Sang Hyun Park; Julia Schnabel. Springer Science and Business Media Deutschland GmbH, 2021. p. 138-146 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12928 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Opportunistic Screening of Osteoporosis Using Plain Film Chest X-Ray

AU - Wang, Fakai

AU - Zheng, Kang

AU - Wang, Yirui

AU - Zhou, Xiaoyun

AU - Lu, Le

AU - Xiao, Jing

AU - Wu, Min

AU - Kuo, Chang Fu

AU - Miao, Shun

PY - 2021

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N2 - Osteoporosis is a common chronic metabolic bone disease that is often under-diagnosed and under-treated due to the limited access to bone mineral density (BMD) examinations, e.g., Dual-energy X-ray Absorptiometry (DXA). In this paper, we propose a method to predict BMD from Chest X-ray (CXR), one of the most common, accessible and low-cost medical image examinations. Our method first automatically detects Regions of Interest (ROIs) of local and global bone structures from the CXR. Then a multi-ROI model is developed to exploit both local and global information in the chest X-ray image for accurate BMD estimation. Our method is evaluated on 1651 CXR cases with ground truth BMD measured by gold standard DXA. The model predicted BMD has a strong correlation with the ground truth (Pearson correlation coefficient 0.853). When applied for osteoporosis screening, it achieves a high classification performance (AUC 0.928). As the first effort in the field using CXR scans to predict the BMD, the proposed algorithm holds a strong potential in early osteoporosis screening and public health promotion.

AB - Osteoporosis is a common chronic metabolic bone disease that is often under-diagnosed and under-treated due to the limited access to bone mineral density (BMD) examinations, e.g., Dual-energy X-ray Absorptiometry (DXA). In this paper, we propose a method to predict BMD from Chest X-ray (CXR), one of the most common, accessible and low-cost medical image examinations. Our method first automatically detects Regions of Interest (ROIs) of local and global bone structures from the CXR. Then a multi-ROI model is developed to exploit both local and global information in the chest X-ray image for accurate BMD estimation. Our method is evaluated on 1651 CXR cases with ground truth BMD measured by gold standard DXA. The model predicted BMD has a strong correlation with the ground truth (Pearson correlation coefficient 0.853). When applied for osteoporosis screening, it achieves a high classification performance (AUC 0.928). As the first effort in the field using CXR scans to predict the BMD, the proposed algorithm holds a strong potential in early osteoporosis screening and public health promotion.

KW - Bone mineral density estimation

KW - Chest X-ray

KW - Multi-ROI model

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M3 - 会议稿件

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SN - 9783030876012

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Predictive Intelligence in Medicine - 4th International Workshop, PRIME 2021, Held in Conjunction with MICCAI 2021, Proceedings

A2 - Rekik, Islem

A2 - Adeli, Ehsan

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A2 - Schnabel, Julia

PB - Springer Science and Business Media Deutschland GmbH

T2 - 4th International Workshop on Predictive Intelligence in Medicine, PRIME 2021, held in conjunction with 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021

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ER -

Wang F, Zheng K, Wang Y, Zhou X, Lu L, Xiao J et al. Opportunistic Screening of Osteoporosis Using Plain Film Chest X-Ray. In Rekik I, Adeli E, Park SH, Schnabel J, editors, Predictive Intelligence in Medicine - 4th International Workshop, PRIME 2021, Held in Conjunction with MICCAI 2021, Proceedings. Springer Science and Business Media Deutschland GmbH. 2021. p. 138-146. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-87602-9_13

Opportunistic Screening of Osteoporosis Using Plain Film Chest X-Ray

Abstract

Publication series

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Bibliographical note

Keywords

UN SDGs

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