Abstract
Objectives
To investigate whether a deep learning model can predict the bone mineral density (BMD) of lumbar vertebrae from unenhanced abdominal computed tomography (CT) images.
Methods
In this Institutional Review Board–approved retrospective study, patients who received both unenhanced CT examinations and dual-energy X-ray absorptiometry (DXA) of the lumbar vertebrae, in two institutions (1 and 2), were included. Supervised deep learning was employed to obtain a convolutional neural network (CNN) model using axial CT images, including the lumbar vertebrae as input data and BMD values obtained with DXA as reference data. For this purpose, 1665 CT images from 183 patients in institution 1, which were augmented to 99,900 (= 1665 × 60) images (noise adding, parallel shift and rotation were performed), were used. Internal (by using data of 45 other patients in institution 1) and external validations (by using data of 50 patients in institution 2) were performed to evaluate the performance of the trained CNN model. Correlations and diagnostic performances were evaluated with Pearson’s correlation coefficient (r) and area under the receiver operating characteristic curve (AUC), respectively.
Results
The estimated BMD values, according to the CNN model (BMDCNN), were significantly correlated with the BMD values obtained with DXA (r = 0.852 (p < 0.001) and 0.840 (p < 0.001) for the internal and external validation datasets, respectively). Using BMDCNN, osteoporosis was diagnosed with AUCs of 0.965 and 0.970 for the internal and external validation datasets, respectively.
Conclusions
Using deep learning, the BMD of lumbar vertebrae could be predicted from unenhanced abdominal CT images.
Key Points
• By applying a deep learning technique, the bone mineral density (BMD) of lumbar vertebrae can be estimated from unenhanced abdominal CT images.
• A strong correlation was observed between the estimated BMD and the BMD obtained with DXA.
• By using the estimated BMD, osteoporosis could be diagnosed with high performance.
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Abbreviations
- AUC:
-
Area under the receiver operating characteristic curve
- BMD:
-
Bone mineral density
- BMDCNN :
-
Bone mineral density obtained with a convolutional neural network
- CNN:
-
Convolutional neural network
- CT:
-
Computed tomography
- DXA:
-
Dual-energy X-ray absorptiometry
- DICOM:
-
Digital imaging and communications in medicine
- ROC:
-
Receiver operating characteristic
- ROI:
-
Region of interest
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Funding
This study has received funding by JSPS KAKENHI Grant Number JP18K15542.
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The scientific guarantor of this publication is Koichiro Yasaka.
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• Retrospective
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• Multicentre study
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Yasaka, K., Akai, H., Kunimatsu, A. et al. Prediction of bone mineral density from computed tomography: application of deep learning with a convolutional neural network. Eur Radiol 30, 3549–3557 (2020). https://doi.org/10.1007/s00330-020-06677-0
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DOI: https://doi.org/10.1007/s00330-020-06677-0