Skip to main content

Research Repository

Advanced Search

A hierarchical opportunistic screening model for osteoporosis using machine learning applied to clinical data and CT images

Liu, Liyu; Si, Meng; Ma, Hecheng; Cong, Menglin; Xu, Quanzheng; Sun, Qinghua; Wu, Weiming; Wang, Cong; Fagan, Michael J.; Mur, Luis A.J.; Yang, Qing; Ji, Bing


Liyu Liu

Meng Si

Hecheng Ma

Menglin Cong

Quanzheng Xu

Qinghua Sun

Weiming Wu

Cong Wang

Michael J. Fagan

Luis A.J. Mur

Qing Yang

Bing Ji


Background: Osteoporosis is a common metabolic skeletal disease and usually lacks obvious symptoms. Many individuals are not diagnosed until osteoporotic fractures occur. Bone mineral density (BMD) measured by dual-energy X-ray absorptiometry (DXA) is the gold standard for osteoporosis detection. However, only a limited percentage of people with osteoporosis risks undergo the DXA test. As a result, it is vital to develop methods to identify individuals at-risk based on methods other than DXA.

Results: We proposed a hierarchical model with three layers to detect osteoporosis using clinical data (including demographic characteristics and routine laboratory tests data) and CT images covering lumbar vertebral bodies rather than DXA data via machine learning. 2210 individuals over age 40 were collected retrospectively, among which 246 individuals’ clinical data and CT images are both available. Irrelevant and redundant features were removed via statistical analysis. Consequently, 28 features, including 16 clinical data and 12 texture features demonstrated statistically significant differences (p < 0.05) between osteoporosis and normal groups. Six machine learning algorithms including logistic regression (LR), support vector machine with radial-basis function kernel, artificial neural network, random forests, eXtreme Gradient Boosting and Stacking that combined the above five classifiers were employed as classifiers to assess the performances of the model. Furthermore, to diminish the influence of data partitioning, the dataset was randomly split into training and test set with stratified sampling repeated five times. The results demonstrated that the hierarchical model based on LR showed better performances with an area under the receiver operating characteristic curve of 0.818, 0.838, and 0.962 for three layers, respectively in distinguishing individuals with osteoporosis and normal BMD.

Conclusions: The proposed model showed great potential in opportunistic screening for osteoporosis without additional expense. It is hoped that this model could serve to detect osteoporosis as early as possible and thereby prevent serious complications of osteoporosis, such as osteoporosis fractures.


Liu, L., Si, M., Ma, H., Cong, M., Xu, Q., Sun, Q., …Ji, B. (2022). A hierarchical opportunistic screening model for osteoporosis using machine learning applied to clinical data and CT images. BMC Bioinformatics, 23, Article 63.

Journal Article Type Article
Acceptance Date Feb 2, 2022
Publication Date Feb 10, 2022
Deposit Date Apr 25, 2022
Publicly Available Date Apr 26, 2022
Journal BMC Bioinformatics
Print ISSN 1471-2105
Electronic ISSN 1471-2105
Publisher Springer Verlag
Peer Reviewed Peer Reviewed
Volume 23
Article Number 63
Keywords Osteoporosis; Opportunistic screening; Machine learning; Clinical data; CT
Public URL


You might also like

Downloadable Citations