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Current Medical Imaging

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ISSN (Print): 1573-4056
ISSN (Online): 1875-6603

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Review Article

A Review on Imaging Techniques and Artificial Intelligence Models for Osteoporosis Prediction

Author(s): S.Arun Inigo*, R. Tamilselvi and M.Parisa Beham

Volume 20, 2024

Published on: 02 August, 2023

Article ID: e080623217779 Pages: 18

DOI: 10.2174/1573405620666230608091911

Price: $65

Abstract

Osteoporosis causes harmful influences on both men and women of all races. Bone mass, also referred to as “bone density,” is frequently used to assess the health of bone. Humans frequently experience bone fractures as a result of trauma, accidents, metabolic bone diseases, and disorders of bone strength, which are typically led by changes in mineral composition and result in conditions like osteoporosis, osteoarthritis, osteopenia, etc. Artificial intelligence holds a lot of promise for the healthcare system. Data collection and preprocessing seem to be more essential for analysis, so bone images from different modalities, such as X-ray, Computed Tomography (CT), and Magnetic Resonance Imaging (MRI), are taken into consideration that help to recognize, classify, and evaluate the patterns in clinical images. This research presents a comprehensive overview of the performance of various image processing techniques and deep learning approaches used to predict osteoporosis through image segmentation, classification, and fault detection. This survey outlined the proposed domain-based deep learning model for image classification in addition to the initial findings. The outcome identifies the flaws in the existing literature's methodology and lays the way for future work in the deep learningbased image analysis model.

[1]
Sözen T, Özışık L, Calik Basaran N. An overview and management of osteoporosis. Eur J Rheumatol 2017; 4(1): 46-56.
[http://dx.doi.org/10.5152/eurjrheum.2016.048] [PMID: 28293453]
[2]
Decreasing bone mass vector image. Available From: https://www.vectorstock.com/royalty-free-vector/decreasing-bone-mass-vector-19746901
[3]
Smets J, Shevroja E, Hügle T, Leslie WD, Hans D. Machine learning solutions for osteoporosis—A review. J Bone Miner Res 2021; 36(5): 833-51.
[http://dx.doi.org/10.1002/jbmr.4292]
[4]
Yadav DP, Rathor S. Bone fracture detection and classification using deep learning approach. International Conference on Power Electronics & IoT Applications in Renewable Energy and its Control (PARC). Mathura, India. 2020; pp. 282-5.
[5]
Surgeon General of the United States, National Institutes of Health and National Osteoporosis Foundation.. Bone Health and Osteoporosis: A Report of the Surgeon General. Rockville (MD): Office of the Surgeon General (US) 2004. Available From: https://www.ncbi.nlm.nih.gov/books/NBK45515/figure/ch4.f4/
[6]
Yamamoto N, Sukegawa S, Kitamura A, et al. Deep learning for osteoporosis classification using hip radiographs and patient clinical covariates. Biomolecules 2020; 10(11): 1534.
[http://dx.doi.org/10.3390/biom10111534]
[7]
Ferizia U, Honigb S, Chang G. Artificial intelligence, osteoporosis and fragility fractures. Wolters Kluwer Health, Inc. 2019.
[8]
Imaging of Musculoskeletal Disorders. Available From: https://www.radiologymasterclass.co.uk/tutorials/musculoskeletal/imaging-joints-bones/osteoporosis_x-ray
[9]
Hsieh C-I, Zheng K, Lin C. Automated bone mineral density prediction and fracture risk assessment using plain radiographs via deep learning. Nat Commun 2021.
[10]
Osteoporosis Awareness. Available From: https://bergmanross.co.za/osteoporosis/
[11]
Fathima NSM, Tamilselvi R, Parisa Beham M. A survey on osteoporosis detection methods with a focus on X-ray and dexa images. J Inst Electron Telecommun Eng 2020; 68(6): 4640-664.
[12]
Patil KA, Mahendra Prashanth KV, Ramalingaiah Dr A. A comparative study on the detection of osteoporosis using deep learning methods: A review. International Journal of Orthopaedics Sciences 2021; 7(3): 108-5.
[13]
Areeckal AS, Kocher M, Sumam DS. Current and emerging diagnostic imaging-based techniques for assessment of osteoporosis and fracture risk. IEEE Rev Biomed Eng 2019; 12: 254.
[PMID: 29994405]
[14]
Wani IM, Arora S. Computer-aided diagnosis systems for osteoporosis detection: A comprehensive survey. Med Biol Eng Comput 2020; 58(9): 1873-917.
[http://dx.doi.org/10.1007/s11517-020-02171-3] [PMID: 32583141]
[15]
Cruz AS, Lins HC, Medeiros RVA, Filho JMF, da Silva SG. Artificial intelligence on the identification of risk groups for osteoporosis, a general review. Biomed Eng Online 2018; 17(1): 12.
[http://dx.doi.org/10.1186/s12938-018-0436-1] [PMID: 29378578]
[16]
Trajanoska K, Rivadeneira F. The genetic architecture of osteoporosis and fracture risk. Bone 2019; 126: 2-10.
[http://dx.doi.org/10.1016/j.bone.2019.04.005] [PMID: 30980960]
[17]
Symptoms of osteoporosis. Available From: https://www.lispine.com/blog/symptoms-of-osteoporosis/
[18]
Kajla V, Gupta A, Khatak A. Analysis of X-ray images with image processing techniques: A review. International Conference on Computing Communication and Automation. Greater Noida, India. 2018; pp. 1-4.
[19]
Guan B, Zhang G, Yao J, Wang X. Arm fracture detection in X-rays based on improved deep convolutional neural network. Comput Electr Eng 2018; 81: 106530.
[20]
Seo Kanghyen, Kim Seung Hun, Kang Seong Hyeon, Park Jongwoon, Lee Chang Lae, Lee Youngjin. The effects of total variation (TV) technique for noise reduction in radio-magnetic X-ray image: Quantitative study. J Magnet 2016; 21: 593-8.
[21]
Mahendran SK, Santhosh Baboo S. An enhanced tibia fracture detection tool using image processing and classification fusion techniques in X-ray images. Global J Comp Sci Technol 2011; 11(14): 23-8.
[22]
Kirti T, Jitendra K, Ashok S. Poisson noise reduction from X-ray images by region classification and response median filtering. Sādhanā. 2017; 42: pp. 855-63.
[23]
Zhu H, Sun W, Wu M, Guan G. Pre-processing of x-ray medical image based on improved temporal recursive self-adaptive filter. The 9th International Conference for Young Computer Scientists. Hunan, China. 2008; pp. 758-63.
[24]
Bekkanti A, Gogulamudi S, Karimunnisa S. Enhanced computerized bone fracture detection using harris corner detection. International Conference on Smart Electronics and Communication. Trichy, India. 2020; pp. 572-6.
[http://dx.doi.org/10.1109/ICOSEC49089.2020.9215240]
[25]
Folasade Olubusola Isinkaye. Abiodun Gabriel Aluko, Olayinka Ayodele Jongbo, Segmentation of medical x-ray bone image using different image processing techniques. IJ Image, Graphics and Signal Processing 2021; 5(3): 27-40.
[26]
Zeelan Basha CMAK, Maruthi Padmaja T, Balaji GN. Automatic X-ray image classification system. In: Satapathy S, Bhateja V, Das S, Eds. Smart Computing and Informatics. Singapore: Springer 2017; vol 78.
[27]
Basha CZ, Rohini G, Jayasri AV. Enhanced and effective computerized classification of X-ray images. International Conference on Electronics and Sustainable Communication Systems. Coimbatore, India. 2020; pp. 86-91.
[28]
Anil K. An improved segmentation algorithm for X-ray images based on adaptive thresholding classification. 2019.
[29]
Hrosik RC, Tuba E, Dolicanin E, Jovanovic R. Brain image segmentation based on firefly algorithm combined with K-means clustering. Stud Inform Control 2019; 28(2): 167-76.
[30]
Peruri S, Vamsi J. Bone fracture detection using image processing. Int J Sci Dev Res 2020; 5(6): 329-4.
[31]
Sahu Barnali, Jena Shweta, Jagadev Alok Kumar. A model for comparative analysis of medical bone X-ray images using image segmentation. Int J Telemed Clin Prac 2016; 1(3): 199-208.
[32]
Zeelan Basha CMAK, Maruthi Padmaja T, Balaji GN. An effective and reliable computer automated technique for bone fracture detection. EAI Endorsed Trans Pervasive Health Technol 2020; 5(18): 162402.
[33]
Nordin C. Screening for osteoporosis: U.S. Preventive Services Task Force recommendation statement. Ann Intern Med 2011; 155(4): 276.
[http://dx.doi.org/10.7326/0003-4819-155-4-201108160-00021] [PMID: 21844561]
[34]
Zhang J, Wu F, Chang W, Kong D. Techniques and algorithms for hepatic vessel skeletonization in medical images: A survey. Entropy 2022; 24(4): 465.
[http://dx.doi.org/10.3390/e24040465] [PMID: 35455128]
[35]
Xiangbin LS, Liu S, Zhang Y. A review of deep-learning-based medical image segmentation methods. MDPI Sustainability 2021; 13(3): 1224.
[36]
Ronneberger O, Fischer P, Brox T. U-Net: Convolutional networks for biomedical image segmentation. In: Narab N, Hornegger J, Wells W, Fraagi A, Eds. Computing and Computer-assisted intervention 9351
[37]
Perry J, Nguyen T, Peter M, Perry J. Improved CT-based osteoporosis assessment with a fully automated deep learning tool. Radiol Artif Intell 2022; 4(5): e220042.
[38]
Sukegawa S, Yoshii K, Hara T, et al. Deep neural networks for dental implant system classification. Biomolecules 2020; 10(7): 984.
[http://dx.doi.org/10.3390/biom10070984] [PMID: 32630195]
[39]
He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit. Las Vegas, NV, USA. 2016; pp. 770-8.
[40]
Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Boston, MA, USA. 2015; pp. 1-9.
[41]
Tan M, Le QV. EfficientNet: Rethinking model scaling for convolutional neural networks.36th International Conference on Machine Learning. San Diego, CA, USA 2019; pp. 9-15.
[42]
Gao L, Jiao T, Feng Q, Wang W. Application of artificial intelligence in diagnosis of osteoporosis using medical images: A systematic review and meta-analysis. Osteoporosis International 2021; 32(7): 1279-86.
[43]
Chen Y, Yang T, Gao X, Xu A. Hybrid deep learning model for risk prediction of fracture in patients with diabetes and osteoporosis. Frontiers of Medicine 2022; 16(3): 496-506.
[PMID: 34448125]
[44]
Fabijańska A. Segmentation of corneal endothelium images using a U-Net-based convolutional neural network. Artif Intell Med 2018; 88: 1-13.
[http://dx.doi.org/10.1016/j.artmed.2018.04.004] [PMID: 29680687]
[45]
Sevastopolsky A. Optic disc and cup segmentation methods for glaucoma detection with modification of U-Net convolutional neural network. Pattern Recognit Image Anal 2017; 27(3): 618-24.
[http://dx.doi.org/10.1134/S1054661817030269]
[46]
Nissinen T, Suoranta S, Saavalainen T, et al. Detecting pathological features and predicting fracture risk from dual-energy X-ray absorptiometry images using deep learning. Bone Rep 2021; 14: 101070.
[http://dx.doi.org/10.1016/j.bonr.2021.101070]
[47]
Yasaka K, Akai H, Kunimatsu A, Kiryu S, Abe O. Prediction of bone mineral density from computed tomography: Application of deep learning with a convolutional neural network. Eur Radiol 2020; 30(6): 3549-57.
[http://dx.doi.org/10.1007/s00330-020-06677-0] [PMID: 32060712]
[48]
Fang Y, Lei W, Chen X, et al. Opportunistic osteoporosis screening in multi-detector CT images using deep convolutional neural networks. Eur Radiol 2021; 31(4): 1831-92.
[PMID: 33001308]
[49]
Krishnaraj A, Barrett S, Bregman-Amitai O, et al. Simulating dual-energy X-ray absorptiometry in CT using deep-learning segmentation cascade. J Am Coll Radiol 2019; 16(10): 1473-9.
[http://dx.doi.org/10.1016/j.jacr.2019.02.033] [PMID: 30982683]
[50]
Dagan N, Elnekave E, Barda N, et al. Automated opportunistic osteoporotic fracture risk assessment using computed tomography scans to aid in FRAX underutilization. Nat Med 2020; 26(1): 77-82.
[http://dx.doi.org/10.1038/s41591-019-0720-z] [PMID: 31932801]
[51]
Guan B, Zhang G, Yao J. Arm fracture detection in X-rays based on improved deep convolutional neural network. Comput Electr Eng 2020; 81: 106530.
[52]
Ren S, He K, Girshick R, Sun J. Faster R-CNN: Towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 2017; 39(6): 1137-49.
[http://dx.doi.org/10.1109/TPAMI.2016.2577031] [PMID: 27295650]
[53]
Lin T, Doll P, Girshick R, et al. Feature pyramid networks for object detection. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu, HI, USA. 2017; pp. 936-44.
[http://dx.doi.org/10.1109/CVPR.2017.106]
[54]
Cai Z, Vasconcelos N. Cascade R-CNN: Delving into high quality object detection. IEEE/CVF conf comput vis pattern recogni. Salt Lake City, UT, USA. 2018; pp. 6154-62.
[55]
Guan B, Yao J, Zhang G, Wang X. Thigh fracture detection using deep learning method based on new dilated convolutional feature pyramid network. Pattern Recognit Lett 2019; 125: 521-6.
[http://dx.doi.org/10.1016/j.patrec.2019.06.015]
[56]
Yao L, Guan X, Song X, et al. Rib fracture detection system based on deep learning. Sci Rep 2021; 11(1): 23513.
[http://dx.doi.org/10.1038/s41598-021-03002-7]
[57]
Jang R, Choi JH, Kim N, Chang JS, Yoon PW, Kim CH. Prediction of osteoporosis from simple hip radiography using deep learning algorithm. Sci Rep 2021; 11(1): 19997.
[http://dx.doi.org/10.1038/s41598-021-99549-6] [PMID: 34620976]
[58]
Anam M, Ponnusamy MH. Osteoporosis prediction in trabecular bone using machine learning: A review. Comput Mater Continua 2021; 67(1): 89-105.
[59]
Shen D, Wu G, Suk HI. Deep Learning in Medical Image Analysis. Annu Rev Biomed Eng 2017; 19(1): 221-48.
[http://dx.doi.org/10.1146/annurev-bioeng-071516-044442] [PMID: 28301734]
[60]
Jia W, Xiangjian H, Kennedy P. “Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges”. J Digit Imaging 2019.
[61]
Marwa F, Zahzah E, Bouallegue K, Machhout M. Deep learning based neural network application for automatic ultrasonic computed tomographic bone image segmentation. Multimedia Tools Appl 2022; 81(10): 13537-62.
[http://dx.doi.org/10.1007/s11042-022-12322-3] [PMID: 35194385]
[62]
Rodríguez-Esparza E, Zanella-Calzada LA, Oliva D, et al. An efficient Harris hawks-inspired image segmentation method. Expert Syst Appl 2020; 155: 113428.
[http://dx.doi.org/10.1016/j.eswa.2020.113428]
[63]
Hussain D, Han SM. Computer-aided osteoporosis detection from DXA imaging. Comput Methods Programs Biomed 2019; 173: 87-107.
[http://dx.doi.org/10.1016/j.cmpb.2019.03.011] [PMID: 31046999]
[64]
Zhang B, Jia C, Wu R. Improving rib fracture detection accuracy and reading efficiency with deep learning-based detection software: A clinical evaluation. Br J Radiol 2021; 94(1118): 202008-70.
[PMID: 33332979]
[65]
Aasis Unnanuntana MD, Brian P. The assessment of fracture risk. J Bone Jt Surg 2010; 92(3): 743-53.
[66]
Saranya A, Kottilingam Dr K. A survey on bone fracture identification techniques using quantitative and learning based algorithms. 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS). Coimbatore, India. 2021; pp. 241-8.
[67]
Available From: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3991939/
[68]
Du Getao, Cao Xu, Liang Jimin, Chen Xueli. Medical image segmentation based on U-net: A review. J Imag Sci Technol 2020. Preprint
[69]
Zhao C, Xiang S, Wang Y, et al. Context-aware network fusing transformer and V-Net for semi-supervised segmentation of 3D center atrium. Expert Syst Appl 2023; 214: 119105.
[http://dx.doi.org/10.1016/j.eswa.2022.119105]
[70]
Sheny W, Xuy W, Zhang H. Automatic segmentation of the femur and tibia bones from X-ray images based on pure dilated residual U-net. Inverse Probl Imaging (Springfield) 2021; 15(6): 1333-346.
[71]
Chebli A, Djebbar A, Marouani HF. Semi-supervised learning for medical application: A survey. Proceedings of the 2018 International Conference on Applied Smart Systems. Medea, Algeria. 2019; pp. 1-9.

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