Data Modeling
Mathematical and Computational Data Modeling
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Volume 6 Number 1 2025
RESEARCH ARTICLE (Open Access)
Deep Learning-Based Osteoporosis Screening from Lumbar Spine X-ray Images: A CNN Approach Compared with DXA
Md. Nesar Uddin1*, Kamruzzaman Mithu 1, Md. Ataur Rahman1, Shahanara Begum 2, Khondaker Abdullah Al Mamun1
Data Modeling 6 (1) 1-8 https://doi.org/10.25163/data.6110745
Submitted: 31 December 2024 Revised: 28 February 2025 Accepted: 06 March 2025 Published: 08 March 2025
Abstract
Osteoporosis, a condition often progressing silently until fracture occurs, continues to pose a substantial clinical and public health burden worldwide. While dual-energy X-ray absorptiometry (DXA) remains the gold standard for diagnosis, its limited accessibility and underutilization raise important questions about alternative screening strategies. In this context, the present study explores whether deep learning—specifically convolutional neural networks (CNNs)—might offer a complementary approach using routinely acquired lumbar spine X-ray images. A retrospective multicenter dataset comprising 1,255 postmenopausal women and 2,510 radiographic images was analyzed. Regions of interest were manually identified, and a dual-channel CNN model was developed using anteroposterior and lateral views. Model predictions were evaluated against DXA-derived bone mineral density classifications based on World Health Organization criteria. Performance was assessed using area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. The model demonstrated moderate diagnostic capability. In the validation dataset, osteoporosis detection achieved an AUC of 0.786, with sensitivity of 60.6% and specificity of 85.5%. In independent test datasets, AUC values ranged from 0.726 to 0.810, with variability observed across imaging channels and classification categories. Notably, sensitivity for osteopenia detection improved in certain configurations, suggesting differential feature representation across views. Although these findings do not yet support clinical replacement of DXA, they suggest that deep learning applied to routine radiographs may provide a feasible adjunct screening tool. Further refinement, validation, and integration with clinical risk factors will be essential before such approaches can be meaningfully translated into practice.
Keywords: Osteoporosis, Deep Learning, Convolutional Neural Network, Lumbar Spine X-ray, Bone Mineral Density.
References
Amarnath, A. L., Franks, P., Robbins, J. A., Xing, G., & Fenton, J. J. (2015). Underuse and overuse of osteoporosis screening in a regional health system: A retrospective cohort study. Journal of General Internal Medicine, 30(12), 1733–1740.
Brown, C. (2017). Osteoporosis: Staying strong. Nature, 550, S15–S17.
Centers for Medicare & Medicaid Services. (2019). National physician fee schedule. Retrieved April 17, 2019, from http://www.cms.hhs.gov/PFSlookup/
Cheung, A. M., Papaioannou, A., Morin, S., & Osteoporosis Canada Scientific Advisory Council. (2016). Postmenopausal osteoporosis. New England Journal of Medicine, 374, 2096.
Curry, S. J., Krist, A. H., Owens, D. K., Barry, M. J., Caughey, A. B., Davidson, K. W., Doubeni, C. A., Epling, J. W., Kemper, A. R., Kubik, M., Landefeld, C. S., Mangione, C. M., Silverstein, M., Simon, M. A., Tseng, C. W., & Wong, J. B. (2018). Screening for osteoporosis to prevent fractures: U.S. Preventive Services Task Force recommendation statement. JAMA, 319(24), 2521–2531.
Dimai, H. P. (2017). Use of dual-energy X-ray absorptiometry (DXA) for diagnosis and fracture risk assessment: WHO criteria, T- and Z-score, and reference databases. Bone, 104, 39–43.
Erhan, D., Bengio, Y., Courville, A., Manzagol, P.-A., Vincent, P., & Bengio, S. (2010). Why does unsupervised pre-training help deep learning? Journal of Machine Learning Research, 11, 625–660.
Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., & Dean, J. (2019). A guide to deep learning in healthcare. Nature Medicine, 25, 24–29.
Ferizi, U., Honig, S., & Chang, G. (2019). Artificial intelligence, osteoporosis and fragility fractures. Current Opinion in Rheumatology, 31(4), 368–375.
King, A. B., & Fiorentino, D. M. (2011). Medicare payment cuts for osteoporosis testing reduced use despite tests’ benefit in reducing fractures. Health Affairs, 30(12), 2362–2370.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521, 436–444.
Lever, J., Krzywinski, M., & Altman, N. (2016). Model selection and overfitting. Nature Methods, 13(9), 703–704.
National Health Commission of China. (2019). Epidemiological investigation of osteoporosis in China. Retrieved May 15, 2019, from http://www.phsciencedata.cn/Share/jsp/PublishManager/foregroundView/1/9eaadbb3-bd64-4531-9d9f-753ec183f26d.html
Schuit, S. C., van der Klift, M., Weel, A. E., de Laet, C. E., Burger, H., Seeman, E., Hofman, A., Uitterlinden, A. G., & Pols, H. A. (2004). Fracture incidence and association with bone mineral density in elderly men and women: The Rotterdam Study. Bone, 34(1), 195–202.
U.S. Preventive Services Task Force. (2002). Screening for osteoporosis in postmenopausal women: Recommendations and rationale. American Family Physician, 66(8), 1430–1432.
Wainwright, S. A., Marshall, L. M., Ensrud, K. E., Cauley, J. A., Black, D. M., Hillier, T. A., Hochberg, M. C., Vogt, M. T., Orwoll, E. S., & Study of Osteoporotic Fractures Research Group. (2005). Hip fracture in women without osteoporosis. Journal of Clinical Endocrinology & Metabolism, 90(5), 2787–2793.
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