Detection and Localization of Spine Disorders from Plain Radiography

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.

PubMed  Google Scholar 

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.

PubMed  Google Scholar 

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.

Article  PubMed  Google Scholar 

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).

Article  PubMed  Google Scholar 

Tang, C., Aggarwal, R.: Imaging for musculoskeletal problems. InnovAiT 6(11), 735–738 (2013).

Article  Google Scholar 

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.

Article  Google Scholar 

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).

PubMed  Google Scholar 

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.

Article  PubMed  Google Scholar 

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.

Article  CAS  Google Scholar 

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.

Article  PubMed  Google Scholar 

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.

Article  PubMed  Google Scholar 

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.

Article  Google Scholar 

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.

Article  Google Scholar 

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.

Article  Google Scholar 

Comments (0)

No login
gif