DOI: http://dx.doi.org/10.18203/issn.2455-4510.IntJResOrthop20212440

Detection of osteoporosis in lumbar spine [L1-L4] trabecular bone: a review article

Kavita Avinash Patil, K. V. Mahendra Prashanth, A. Ramalingaiah

Abstract


The human bones are categorized based on elemental micro architecture and porosity. The porosity of the inner trabecular bone is high that is 40-95% and the nature of the bone is soft and spongy where as the cortical bone is harder and is less porous that is 5 to 15%. Osteoporosis is a disease that normally affects women usually after their menopause. It largely causes mild bone fractures and further stages lead to the demise of an individual. This analysis is on the basis of bone mineral density (BMD) standards obtained through a variety of scientific methods experimented from different skeletal regions. The detection of osteoporosis in lumbar spine has been widely recognized as a promising way to frequent fractures. Therefore, premature analysis of osteoporosis will estimate the risk of the bone fracture which prevents life threats. This paper focuses on the advanced technology in imaging systems and fracture probability analysis of osteoporosis detection. The various segmentation techniques are explored to examine osteoporosis in particular region of the image and further significant attributes are extracted using different methods to classify normal and abnormal (osteoporotic) bones. The limitations of the reviewed papers are more in feature dimensions, lesser accuracy and expensive imaging modalities like computed tomography (CT), magnetic resonance imaging (MRI), and DEXA. To overcome these limitations it is suggested to have less feature dimensions, more accuracy and cost-effective imaging modality like X-ray. This is required to avoid bone fractures and to improve BMD with precision which further helps in the diagnosis of osteoporosis.


Keywords


BMD, DXA, Lumbar spine, Machine learning algorithms, Osteoporosis, T-score

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References


Peterson, Anthony J. Osteoporosis overview. Geriatric Nursing. 2001;22(1):17-23.

https://comportho.com/spine/anatomy-of-the-spine/. Accessed on 7th April 2021.

https://sites.google.com/site/activecarephysiotherapyclinic/lumbar-spine-diseases. Accessed on 7th April 2021.

Poorman, Wyatt G. Rates of mortality in lumbar spine surgery and factors associated with its occurrence over a 10-year period: a study of 803,949 patients in the Nationwide Inpatient Sample. International Journal of Spine Surgery. 2018;12(5):617-23.

Khadilkar, Anuradha V, Mandlik RM. Epidemiology and treatment of osteoporosis in women: an Indian perspective. International journal of women's health. 2015;7:841-50.

Ambrish M. The Asia-pacific regional audit-epidemiology, costs, and burden of osteoporosis in India 2013: a report of international osteoporosis foundation." Indian journal of endocrinology and metabolism. 2014;18(4):449.

Bhaskar B, Phukan P, Sarma K. Prevalence of osteoporosis among vulnerable adults residing in the north-eastern region of India: A preliminary report from a tertiary care referral hospital. Journal of Orthopaedics, Traumatology and Rehabilitation. 2017;9(2):84.

Nutrient Requirements and Recommended Dietary Allowances for Indians: A Report of the Expert Group of the Indian Council of Medical Research; 2009. Hyderabad: National Institute of Nutrition; Indian Council of Medical Research.

Khadilkar A, Das G, Sayyad M. Low calcium intake and hypovitaminosis D in adolescent girls. Arch Dis Child. 2007;92(11):1045.

Khadilkar AV. Vitamin D deficiency in Indian adolescents. Indian Pediatr. 2010;47(9):755-6.

Aggarwal N, Raveendran A, Khandelwal N. Prevalence and related risk factors of osteoporosis in peri- and postmenopausal Indian women. J Midlife Health. 2011;2(2):81-5.

Binu, John A. Bone Health after fifth decade in rural ambulatory South Indian postmenopausal women. Indian journal of community medicine: official publication of Indian Association of Preventive & Social Medicine. 2019;44(3):205.

Areeckal AS, M Kocher SDS. Current and Emerging Diagnostic Imaging-Based Techniques for Assessment of Osteoporosis and Fracture Risk. in IEEE Reviews in Biomedical Engineering. 2019;12:254-68.

https://www.radiologyinfo.org/en/info.cfm?pg=osteoporosis. Accessed on 7th April 2021.

Link TM. Osteoporosis imaging: state of the art and advanced imaging. Radiology. 2019;263(1):3-17.

Kanis JA, McCloskey EV, Johansson H, Oden A, Melton LJ, Khaltaev N. A reference standard for the description of osteoporosis. Bone. 2008;42(3):467-75.

Emami A, Ghadiri H, Rahmim A, Ay M. A novel dual energy method for enhanced quantitative computed tomography. J Instrum. 2018;3(1):P01030.

deJongetal JJ. Fracture repair in the distal radius in postmenopausal women: Afollow-up2yearspostfractureusingHRpQCT. J. Bone Mineral Res. 2016;31(5):1114-22.

Kazakia GJ, Burghardt AJ, Link TM, Majumdar S. Variations in morphological and biomechanical indices at the distal radius in subjects with identical BMD. J. Biomechanics. 2011;44(2):257-66.

Hans D, Hartl F, Krieg M. Device-specific weighted T-score for two quantitative ultrasounds: Operational propositions for the management of osteoporosis for 65 years and older women in Switzerland. Osteoporosis Int. 2003;14(3):251-8.

Njeh C. Comparison of six calcaneal quantitative ultrasound devices: Precision and hip fracture discrimination. Osteoporosis Int. 2000;11(12):1051-62.

Rosholm A, Hyldstrup L, Baeksgaard L, Grunkin M, Thodberg H. Estimation of bone mineral density by digital X-ray radiogrammetry: Theoretical background and clinical testing. Osteoporosis Int. 2001;12(11):961-9.

Omiotek Z, Dzierżak R, Uhlig S. Fractal analysis of the computed tomography images of vertebrae on the thoraco-lumbar region in diagnosing osteoporotic bone damage. Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine. 2009;233(12):1269-81.

Nam K, Seo I, Kim D, Lee J, Choi B, Han I. Machine Learning Model to Predict Osteoporotic Spine with Hounsfield Units on Lumbar Computed Tomography. Journal of Korean Neurosurgical Society. 2019; 62(4):442-9.

Picazo ML. 3-D Subject-Specific Shape and Density Estimation of the Lumbar Spine from a Single Anteroposterior DXA Image Including Assessment of Cortical and Trabecular Bone. IEEE Transactions on Medical Imaging. 2018;37(12):2651-62.

Shaker AS. Detection and Segmentation of Osteoporosis in Human Body using Recurrent Neural Network. IJAST. 2020;29(2);1055-66.

Yousefi H, Salehi E, Sheyjani OS, Ghanaatti H. Lumbar Spine Vertebral Compression Fracture Case Diagnosis Using Machine Learning Methods on CT images. 2019 4th International Conference on Pattern Recognition and Image Analysis (IPRIA), Tehran, Iran. 2019;179-84.

Korchiyne R. New Approach Based on Multifractal Spectrum Features for Detection of Osteoporosis. 2018 International Symposium on Advanced Electrical and Communication Technologies (ISAECT), Rabat, Morocco. 2018;1-5.

Wang Y, Yao J, Burns JE, Summers R. Osteoporotic and neoplastic compression fracture classification on longitudinal CT. 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), Prague. 2016;1181-4.

Frighetto-Pereira L, Menezes-Reis R, Metzner GA, Rangayyan RM, Nogueira-Barbosa MH, Azevedo-Marque PM. Classification of vertebral compression fractures in magnetic resonance images using shape analysis. 2015 E-Health and Bioengineering Conference (EHB), Iasi. 2015;1-4.

Devikanniga, D, Raj JS. Classification of osteoporosis by artificial neural network based on monarch butterfly optimisation algorithm. Healthcare technology letters vol. 2018;5(2);70-5.

Valentinitsch A, Trebeschi S, Kaesmacher J. Opportunistic osteoporosis screening in multi-detector CT images via local classification of textures. Osteoporos Int. 2019;30:1275-85.

Czum, Julianna M. Simulated Lumbar Dual-Energy X-Ray Absorptiometry With Machine Learning Algorithms: Why Radiologists Who Interpret Abdominal CTs Should Care. Journal of the American College of Radiology. 2019;16(10):1471-2.

Tang, Chao, Zhang W, Li H, Li L, Li Z, Cai A et al. CNN-based Automatic Detection of Bone Conditions via Diagnostic CT Images for Osteoporosis Screening. arXiv preprint arXiv. 2019;1910.06777.

Lee, Sangwoo, Choe EK, Kang HY, Yoon JW, Kim HS. The exploration of feature extraction and machine learning for predicting bone density from simple spine X-ray images in a Korean population. Skeletal Radiology. 2020;49(4):613-8.

Saad MM, Ahmed AT, Mohamed KE. Role of lumbar spine signal intensity measurement by MRI in the diagnosis of osteoporosis in post-menopausal women. Egypt J RadiolNucl Med. 2019;50:35.

Muehlematter UJ, Mannil M, Becker AS, Vokinger KN, Finkenstaedt T, Osterhoff G et al. Vertebral body insufficiency fractures: detection of vertebrae at risk on standard CT images using texture analysis and machine learning. EurRadiol. 2019;29(5):2207-17.

Zhao Y, Huang M, Ding J, Zhang X, Spuhler K, Hu S et al. Prediction of Abnormal Bone Density and Osteoporosis From Lumbar Spine MR Using Modified Dixon Quant in 257 Subjects With Quantitative Computed Tomography as Reference. J MagnReson Imaging. 2019;49(2):390-9.

Frighetto-Pereira, Lucas. Shape, texture and statistical features for classification of benign and malignant vertebral compression fractures in magnetic resonance images. Computers in biology and medicine. 2016;73:147-56.

Kilic N, Hosgormez E. Automatic Estimation of Osteoporotic Fracture Cases by Using Ensemble Learning Approaches. J Med Syst. 2016;40:61.

Sungkhun S, Keo C, Khoeun R, Chinnasarn K, Rasmequan S, Rodtook A. Automated multiple lesion identification on vertebral spine using modified average intensity. 2016 International Conference on Advanced Informatics: Concepts, Theory and Application (ICAICTA), George Town. 2016;1-6.

Aventaggiato M. Validation of an automatic segmentation method to detect vertebral interfaces in ultrasound images. in IET Science, Measurement & Technology. 2016;10(1):18-27.

Casciaro S, Conversano F, Pisani P, Casciaro E, Muratore M. Ultrasound Osteoporosis Score: A novel parameter for the estimation of spine mineral density," 2015 6th European Symposium on Ultrasonic Characterization of Bone, Corfu. 2015;1-4.

Wei-Liang T. Osteoporosis screening using areal bone mineral density estimation from diagnostic CT images. Academic radiology. 2020;19(10):1273-82.

Al-Helo S, Alomari RS, Ghosh S. Compression fracture diagnosis in lumbar: a clinical CAD system. Int J CARS. 2013;8:461-9.

Pickhardt PJ, Lee LJ, del Rio AM. Simultaneous screening for osteoporosis at CT colonography: bone mineral density assessment using MDCT attenuation techniques compared with the DXA reference standard. J Bone Miner Res. 2011;26(9):2194-203.

Saville PD. The syndrome of spinal osteoporosis. Clinics Endocrinol. Metabolism. 1973;2(2):177-85.

Available at: http://neuros.net/en/osteoporosis.