Highlights
- •Attenuation values can be obtained from vertebrae using a convolutional neural network.
- •An automated, deep learning-based approach matches results of previous studies.
- •Artificial Intelligence could be used for opportunistic osteoporosis screening.
Abstract
Background
Osteoporosis is an underdiagnosed and undertreated disease worldwide. Recent studies
have highlighted the use of simple vertebral trabecular attenuation values for opportunistic
osteoporosis screening. Meanwhile, machine learning has been used to accurately segment
large parts of the human skeleton.
Purpose
To evaluate a fully automated deep learning-based method for lumbar vertebral segmentation
and measurement of vertebral volumetric trabecular attenuation values.
Material and methods
A deep learning-based method for automated segmentation of bones was retrospectively
applied to non-contrast CT scans of 1008 patients (mean age 57 years, 472 female,
536 male). Each vertebral segmentation was automatically reduced by 7 mm in all directions
in order to avoid cortical bone. The mean and median volumetric attenuation values
from Th12 to L4 were obtained and plotted against patient age and sex. L1 values were
further analyzed to facilitate comparison with previous studies.
Results
The mean L1 attenuation values decreased linearly with age by −2.2 HU per year (age > 30,
95% CI: −2.4, −2.0, R2 = 0.3544). The mean L1 attenuation value of the entire population cohort was 140
HU ± 54.
Conclusions
With results closely matching those of previous studies, we believe that our fully
automated deep learning-based method can be used to obtain lumbar volumetric trabecular
attenuation values which can be used for opportunistic screening of osteoporosis in
patients undergoing CT scans for other reasons.
Keywords
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Article info
Publication history
Published online: August 26, 2021
Accepted:
August 13,
2021
Received in revised form:
July 30,
2021
Received:
April 26,
2021
Identification
Copyright
© 2021 Elsevier Inc. All rights reserved.
