Lumbar Bone Mineral Density Estimation from Chest X-ray Images: Anatomy-aware Attentive Multi-ROI Modeling

Osteoporosis is a common chronic metabolic bone disease often under-diagnosed and under-treated due to the limited access to bone mineral density (BMD) examinations, e.g ., via Dual-energy X-ray Absorptiometry (DXA). This paper proposes a method to predict BMD from Chest X-ray (CXR), one of the most...

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Bibliographic Details
Published in:IEEE transactions on medical imaging 2023-01, Vol.42 (1), p.1-1
Main Authors: Wang, Fakai, Zheng, Kang, Lu, Le, Xiao, Jing, Wu, Min, Kuo, Chang-Fu, Miao, Shun
Format: Article
Language:English
Subjects:
Absorptiometry, Photon - methods
Algorithms
Biomedical imaging
Bone Density
Bone diseases
Bone Mineral Density
Bones
Chest
Chest X-ray Imaging
Coders
Correlation coefficient
Correlation coefficients
Deep Self-Attention
Dual energy X-ray absorptiometry
Estimation
Feature extraction
Humans
Lumbar Vertebrae - diagnostic imaging
Medical imaging
Multi-ROI Modeling
Osteoporosis
Osteoporosis - diagnostic imaging
Osteoporosis Screening
Public health
Radiography
Transformers
X-ray imaging
X-Rays
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Summary:Osteoporosis is a common chronic metabolic bone disease often under-diagnosed and under-treated due to the limited access to bone mineral density (BMD) examinations, e.g ., via Dual-energy X-ray Absorptiometry (DXA). This paper proposes a method to predict BMD from Chest X-ray (CXR), one of the most commonly accessible and low-cost medical imaging examinations. Our method first automatically detects Regions of Interest (ROIs) of local CXR bone structures. Then a multi-ROI deep model with transformer encoder is developed to exploit both local and global information in the chest X-ray image for accurate BMD estimation. Our method is evaluated on 13719 CXR patient cases with ground truth BMD measured by the gold standard DXA. The model predicted BMD has a strong correlation with the ground truth (Pearson correlation coefficient 0.894 on lumbar 1). When applied in osteoporosis screening, it achieves a high classification performance (average AUC of 0.968 ). As the first effort of using CXR scans to predict the BMD, the proposed algorithm holds strong potential for early osteoporosis screening and public health promotion.
ISSN:0278-0062
1558-254X
DOI:10.1109/TMI.2022.3209648