Elfering A, Mannion AF. Epidemiology and risk factors of spinal disorders. In: Boos N, Aebi M, editors. Spinal disorders: fundamentals of diagnosis and treatment. Berlin (DE): Springer; 2008.
Alshami, A.M., 2015. Prevalence of spinal disorders and their relationships with age and gender. Saudi medical journal, 36(6), p.725.
Article PubMed PubMed Central Google Scholar
Manek NJ, MacGregor AJ. Epidemiology of back disorders: prevalence, risk factors, and prognosis. Curr Opin Rheumatol. 2005;17:134–140.
Andersson GB. Epidemiological features of chronic low-back pain. Lancet. 1999;354:581–585.
Article CAS PubMed Google Scholar
Wong CC, McGirt MJ. Vertebral compression fractures: a review of current management and multimodal therapy. J Multidiscip Healthc. 2013;6:205–14. Epub 2013/07/03. https://doi.org/10.2147/jmdh.S31659. PubMed PMID: 23818797; PMCID: PMC3693826.
Brinjikji, W., Luetmer, P.H., Comstock, B., Bresnahan, B.W., Chen, L.E., Deyo, R.A., Halabi, S., Turner, J.A., Avins, A.L., James, K. and Wald, J.T., 2015. Systematic literature review of imaging features of spinal degeneration in asymptomatic populations. American journal of neuroradiology, 36(4), pp.811-816.
Article CAS PubMed PubMed Central Google Scholar
McCarthy J, Davis A. Diagnosis and Management of Vertebral Compression Fractures. Am Fam Physician. 2016;94(1):44-50. Epub 2016/07/09. PubMed PMID: 27386723.
Alexandru, D. and So, W., 2012. Evaluation and management of vertebral compression fractures. The Permanente Journal, 16(4), p.46.
Article PubMed PubMed Central Google Scholar
Fehlings, M.G., Tetreault, L., Nater, A., Choma, T., Harrop, J., Mroz, T., Santaguida, C. and Smith, J.S., 2015. The aging of the global population: the changing epidemiology of disease and spinal disorders. Neurosurgery, 77, pp.S1-S5.
Priolo, F., Cerase, A.: The current role of radiography in the assessment of skeletal tumors and tumor-like lesions. European Journal of Radiology 27, S77–S85 (1998).
Tang, C., Aggarwal, R.: Imaging for musculoskeletal problems. InnovAiT 6(11), 735–738 (2013).
Santiago, F.R., Ramos-Bossini, A.J.L., Wáng, Y.X.J. and Zúñiga, D.L., 2020. The role of radiography in the study of spinal disorders. Quantitative imaging in medicine and surgery, 10(12), p.2322.
Lenchik L, Rogers LF, Delmas PD, Genant HK. Diagnosis of osteoporotic vertebral fractures: importance of recognition and description by radiologists. AJR Am J Roentgenol. 2004;183(4):949–58. Epub 2004/09/24. https://doi.org/10.2214/ajr.183.4.1830949. PubMed PMID: 15385286.
Pinto, A., Berritto, D., Russo, A., Riccitiello, F., Caruso, M., Belfiore, M.P., Papapietro, V.R., Carotti, M., Pinto, F., Giovagnoni, A., et al.: Traumatic fractures in adults: Missed diagnosis on plain radiographs in the emergency department. Acta Bio Medica: Atenei Parmensis 89(1), 111 (2018).
Bruno, M.A., Walker, E.A. and Abujudeh, H.H., 2015. Understanding and confronting our mistakes: the epidemiology of error in radiology and strategies for error reduction. Radiographics, 35(6), pp.1668-1676.
Gehlbach SH, Bigelow C, Heimisdottir M, May S, Walker M, et al. Recognition of vertebral fracture in a clinical setting. Osteoporosis Int. 2000;11(7):577–82.
Trockel, M.T., Menon, N.K., Rowe, S.G., Stewart, M.T., Smith, R., Lu, M., Kim, P.K., Quinn, M.A., Lawrence, E., Marchalik, D. and Farley, H., 2020. Assessment of physician sleep and wellness, burnout, and clinically significant medical errors. JAMA network open, 3(12), pp.e2028111-e2028111.
Van Leeuwen, K.G., de Rooij, M., Schalekamp, S., van Ginneken, B. and Rutten, M.J., 2022. How does artificial intelligence in radiology improve efficiency and health outcomes?. Pediatric Radiology, pp.1–7.
Mayo, R.C.; Kent, D.; Sen, L.C.; Kapoor, M.; Leung, J.W.T.; Watanabe, A.T. Reduction of False-Positive Markings on Mammograms: A Retrospective Comparison Study Using an Artificial Intelligence-Based CAD. J. Digit. Imaging 2019, 32, 618–624.
Article PubMed PubMed Central Google Scholar
Fraiwan, M., Audat, Z., Fraiwan, L. and Manasreh, T., 2022. Using deep transfer learning to detect scoliosis and spondylolisthesis from X-ray images. Plos one, 17(5), p.e0267851.
Article CAS PubMed PubMed Central Google Scholar
Dong, Q., Luo, G., Lane, N.E., Lui, L.Y., Marshall, L.M., Kado, D.M., Cawthon, P., Perry, J., Johnston, S.K., Haynor, D. and Jarvik, J.G., 2022. Deep learning classification of spinal osteoporotic compression fractures on radiographs using an adaptation of the genant semiquantitative criteria. Academic radiology, 29(12), pp.1819-1832.
Article PubMed PubMed Central Google Scholar
Naguib, S.M., Hamza, H.M., Hosny, K.M., Saleh, M.K. and Kassem, M.A., 2023. Classification of cervical spine fracture and dislocation using refined pre-trained deep model and saliency map. Diagnostics, 13(7), p.1273.
Article PubMed PubMed Central Google Scholar
Xu, F., Xiong, Y., Ye, G., Liang, Y., Guo, W., Deng, Q., Liang, Z. and Zeng, X., 2023. Deep learning-based artificial intelligence model for classification of vertebral compression fractures: A multicenter diagnostic study. Frontiers in Endocrinology, 14, p.1025749.
Article PubMed PubMed Central Google Scholar
Saravagi, D., Agrawal, S., Saravagi, M., Chatterjee, J.M. and Agarwal, M., 2022. Diagnosis of lumbar spondylolisthesis using optimized pretrained CNN models. Computational Intelligence and Neuroscience, 2022.
Varçin, F., Erbay, H., Çetin, E., Çetin, İ. and Kültür, T., 2019, September. Diagnosis of lumbar spondylolisthesis via convolutional neural networks. In 2019 International Artificial Intelligence and Data Processing Symposium (IDAP) (pp. 1–4). IEEE.
Varçın, F., Erbay, H., Çetin, E., Çetin, İ. and Kültür, T., 2021. End-to-end computerized diagnosis of spondylolisthesis using only lumbar X-rays. Journal of Digital Imaging, 34, pp.85-95.
Article PubMed PubMed Central Google Scholar
Kim, K.C., Cho, H.C., Jang, T.J., Choi, J.M. and Seo, J.K., 2021. Automatic detection and segmentation of lumbar vertebrae from X-ray images for compression fracture evaluation. Computer Methods and Programs in Biomedicine, 200, p.105833.
Trinh, G.M., Shao, H.C., Hsieh, K.L.C., Lee, C.Y., Liu, H.W., Lai, C.W., Chou, S.Y., Tsai, P.I., Chen, K.J., Chang, F.C. and Wu, M.H., 2022. Detection of lumbar spondylolisthesis from X-ray images using deep learning network. Journal of Clinical Medicine, 11(18), p.5450.
Article PubMed PubMed Central Google Scholar
Nguyen, T.P., Chae, D.S., Park, S.J., Kang, K.Y. and Yoon, J., 2021. Deep learning system for Meyerding classification and segmental motion measurement in diagnosis of lumbar spondylolisthesis. Biomedical Signal Processing and Control, 65, p.102371.
Seo, J.W., Lim, S.H., Jeong, J.G., Kim, Y.J., Kim, K.G. and Jeon, J.Y., 2021. A deep learning algorithm for automated measurement of vertebral body compression from X-ray images. Scientific Reports, 11(1), p.13732.
Article CAS PubMed PubMed Central Google Scholar
Kim, D.H., Jeong, J.G., Kim, Y.J., Kim, K.G. and Jeon, J.Y., 2021. Automated vertebral segmentation and measurement of vertebral compression ratio based on deep learning in X-ray images. Journal of digital imaging, 34, pp.853-861.
Article PubMed PubMed Central Google Scholar
Ribeiro, E.A., Nogueira-Barbosa, M.H., Rangayyan, R.M. and Azevedo-Marques, P.M.D., 2012, October. Detection of vertebral compression fractures in lateral lumbar X-ray images. In XXIII Congresso Brasileiro em Engenharia Biomédica (CBEB) (pp. 1–4).
Zhang, J., Lin, H., Wang, H., Xue, M., Fang, Y., Liu, S., Huo, T., Zhou, H., Yang, J., Xie, Y. and Xie, M., 2023. Deep learning system assisted detection and localization of lumbar spondylolisthesis. Frontiers in Bioengineering and Biotechnology, 11.
Thanh, B.P.N. and Nguyen, P., 2023, October. Comparative study of object detection models for abnormality detection on spinal X-ray images. In 2023 International Conference on Multimedia Analysis and Pattern Recognition (MAPR) (pp. 1–5). IEEE.
Zhong, B., Yi, J. and Jin, Z., 2023. AC-Faster R-CNN: an improved detection architecture with high precision and sensitivity for abnormality in spine x-ray images. Physics in Medicine & Biology, 68(19), p.195021.
Kim, G.U., Chang, M.C., Kim, T.U. and Lee, G.W., 2020. Diagnostic modality in spine disease: a review. Asian spine journal, 14(6), p.910.
Article PubMed PubMed Central Google Scholar
Shamshad, F., Khan, S., Zamir, S.W., Khan, M.H., Hayat, M., Khan, F.S. and Fu, H., 2023. Transformers in medical imaging: A survey. Medical Image Analysis, p.102802.
Donnally IC, DiPompeo CM, Varacallo M. Vertebral Compression Fractures. StatPearls. Treasure Island (FL): StatPearls Publishing. Copyright © 2021, StatPearls Publishing LLC.; 2021.
Riggs BL, Melton LJ. The worldwide problem of osteoporosis: insights afforded by epidemiology. Bone. 1995;17(5 suppl):505S-511S.
Article CAS PubMed Google Scholar
Nguyen, H.T., Pham, H.H., Nguyen, N.T., Nguyen, H.Q., Huynh, T.Q., Dao, M. and Vu, V., 2021. VinDr-SpineXR: A deep learning framework for spinal lesions detection and classification from radiographs. In Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part V 24 (pp. 291–301). Springer International Publishing.
Pham, H. H., Nguyen Trung, H., & Nguyen, H. Q. (2021). VinDr-SpineXR: A large annotated medical image dataset for spinal lesions detection and classification from radiographs (version 1.0.0). PhysioNet. https://doi.org/10.13026/q45h-5h59.
Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., ... & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220.
University of Maryland Medical System. (2003). A Patient’s Guide to Lumbar Compression Fracture. Retrieved March 29, 2024, from https://www.umms.org/ummc/health-services/orthopedics/services/spine/patient-guides/lumbar-compression-fractures
Thibault et al., “Volume of lytic vertebral body metastatic disease quantified using computed tomography–based image segmentation predicts fracture risk after spine stereotactic body radiation therapy,” International Journal of Radiation Oncology & Biology & Physics, vol. 97, no. 1, pp. 75-81, 2017.
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