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Prediction of bone mineral density from computed tomography: application of deep learning with a convolutional neural network

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.

Author information

Correspondence to Koichiro Yasaka.

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Guarantor

The scientific guarantor of this publication is Koichiro Yasaka.

Conflict of interest

The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• Retrospective

• Diagnostic or prognostic study

• 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 (2020). https://doi.org/10.1007/s00330-020-06677-0

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Keywords

  • Artificial intelligence
  • Bone mineral density
  • Multidetector computed tomography
  • Deep learning
  • Osteoporosis