Skip to main content
SpringerLink
Log in

Deep Neural Networks for Diagnosis of Osteoporosis: A Review

  • Conference paper
  • First Online:
Proceedings of ICRIC 2019

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 597))

Abstract

Osteoporosis, a pathological disorder of bones affects millions of individuals worldwide and is the most common disease of bones after arthritis. It is caused due to a decrease in mineral density of bones leading to pain, morbidity, fractures and even mortality in some cases. It is diagnosed with DXA, but its high-cost, low-availability and inconsistent BMD measurements do not make it a promising tool for diagnosis of osteoporosis. The computer-aided diagnosis has improved the diagnostics to a large extent. Deep learning-based artificial neural networks have shown state-of-the-art results in the diagnostic field leading to an accurate diagnosis of the disease. This paper reviews the major neural network architectures used for diagnosis of osteoporosis. We reviewed the neural network architectures based on the questionnaires and the deep neural architectures based on image data implemented for diagnosis of osteoporosis and have summarized the future directions which could help in better diagnosis and prognosis of osteoporosis.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Litjens, G., Kooi, T., Bejnordi, B.E., Setio, A.A.A., Ciompi, F., Ghafoorian, M., Van Der Laak, J.A., Van Ginneken, B. and Sánchez, C.I.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)

    Google Scholar 

  2. Birdwell, R.L., Bandodkar, P., Ikeda, D.M.: Computer-aided detection with screening mammography in a university hospital setting. Radiology 236(2), 451–457 (2005)

    Article  Google Scholar 

  3. Li, Q.: Recent progress in computer-aided diagnosis of lung nodules on thin-section CT. Comput. Med. Imaging Graph. 31(4–5), 248–257 (2007)

    Google Scholar 

  4. Ryu, J.H., Kim, H.S., Lee, K.H.: Contour-based algorithms for generating 3D CAD models from medical images. Int. J. Adv. Manuf. Technol. 24(1–2), 112–119 (2004)

    Google Scholar 

  5. Padilla, P., López, M., Górriz, J.M., Ramirez, J., Salas-Gonzalez, D., Álvarez, I.: NMF-SVM based CAD tool applied to functional brain images for the diagnosis of Alzheimer’s disease. IEEE Trans. Med. Imaging 31(2), 207–216 (2012)

    Article  Google Scholar 

  6. Doi, K.: Computer-aided diagnosis in medical imaging: historical review, current status, and future potential. Comput. Med. Imaging Graph. 31(4–5), 198–211 (2007)

    Article  Google Scholar 

  7. Hinton, G., Deng, L., Yu, D., Dahl, G.E., Mohamed, A.R., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T.N., Kingsbury, B.: Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Signal Process. Mag. 29(6), 82–97 (2012)

    Article  Google Scholar 

  8. Deng, L., Hinton, G., Kingsbury, B.: New types of deep neural network learning for speech recognition and related applications: an overview. In: IEEE International Conference on Acoustics, Speech, and Signal Processing 2013, pp. 8599–8603. IEEE (2013)

    Google Scholar 

  9. Chen, L., Wang, S., Fan, W., Sun, J. and Naoi, S.: Beyond human recognition: A CNN-based framework for handwritten character recognition. In: 3rd IAPRAsian Conference on Pattern Recognition 2015, ACPR, pp. 695–699. IEEE (2015)

    Google Scholar 

  10. Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., Ng, A.Y.: Reading digits in natural images with unsupervised feature learning. In: NIPS Workshop on Deep-Learning and Unsupervised Feature Learning, vol. 2011, no. 2, p. 5 (2011)

    Google Scholar 

  11. Teichmann, M., Weber, M., Zoellner, M., Cipolla, R., Urtasun, R.: Multinet: real-time joint semantic reasoning for autonomous driving. In: IEEE Intelligent Vehicles Symposium 2018, vol. IV, pp. 1013–1020. IEEE (2018)

    Google Scholar 

  12. Chen, C., Seff, A., Kornhauser, A., Xiao, J.: Deep driving: learning affordance for direct perception in autonomous driving. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2722–2730 (2015)

    Google Scholar 

  13. Huval, B., Wang, T., Tandon, S., Kiske, J., Song, W., Pazhayampallil, J., AndrilukaM., Rajpurkar, P., Migimatsu, T., Cheng-Yue, R., Mujica, F.: An empirical evaluation of deep learning on highway driving. arXiv preprint arXiv:1504.01716 (2015)

  14. Pereira, S., Pinto, A., Alves, V., Silva, C.A.: Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Trans. Med. Imaging 35(5), 1240–1251 (2016)

    Article  Google Scholar 

  15. Esteva, A., Kuprel, B., Novoa, R.A., Ko, J., Swetter, S.M., Blau, H.M., Thrun, S.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639), 115 (2017)

    Google Scholar 

  16. De Fauw, J., Ledsam, J.R., Romera-Paredes, B., Nikolov, S., Tomasev, N., Blackwell, S., Askham, H., Glorot, X., O’Donoghue, B., Visentin, D., van den Driessche, G.: Clinically applicable deep learning for diagnosis and referral in retinal disease. Nature Med. 24(9), 1342 (2018)

    Google Scholar 

  17. Rajpurkar, P., Irvin, J., Zhu, K., Yang, B., Mehta, H., Duan, T., Ding, D., Bagul, A., Langlotz, C., Shpanskaya, K., Lungren, M.P.: Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning. In: arXiv preprint arXiv:1711.05225 (2017)

  18. Bien, N., Rajpurkar, P., Ball, R.L., Irvin, J., Park, A., Jones, E., Bereket, M., Patel, B.N., Yeom, K.W., Shpanskaya, K., Halabi, S.: Deep-learning-assisted diagnosis for knee magnetic resonance imaging: Development and retrospective validation of MRNet. PLoS Med. 15(11), e1002699 (2018)

    Google Scholar 

  19. Zhang, Y., Yang, L., Chen, J., Fredericksen, M., Hughes, D.P., Chen, D.Z.: Deep adversarial networks for biomedical image segmentation utilizing unannotated images. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 408–416. Springer, Cham (2017)

    Google Scholar 

  20. Liu, W., Wang, Z., Liu, X., Zeng, N., Liu, Y., Alsaadi, F.E.: A survey of deep-neural network architectures and their applications. Neurocomputing 234, 11–26 (2017)

    Article  Google Scholar 

  21. Osteoporosis. Park Ridge, IL, American Academy of Orthopaedic Surgeons, 1986

    Google Scholar 

  22. NIH Consensus Development Panel on Osteoporosis Prevention, Diagnosis, and Therapy. Osteoporosis prevention, diagnosis, and therapy. JAMA 285(6), 785–795 (2001)

    Google Scholar 

  23. Kaplan, F.S.: Osteoporosis. Women’s Health 10(2/3), 95–114 (1985)

    Google Scholar 

  24. Cooper, C., Campion, G. and Melton, L. J.: III. Hip fractures in the elderly: a world wide projection. Osteoporos. Int. 2, 285–289 (1992)

    Google Scholar 

  25. World Health Organization (ed.): Assessment of Fracture Risk and Its Application to Screening for Postmenopausal Osteoporosis. World Health Organization, Geneva (1994)

    Google Scholar 

  26. Kanis J.A.: Assessment of fracture risk and its application to screening for postmenopausal osteoporosis: synopsis of a WHO report. WHO study group, Osteoporos. Int., 368–814 (1994)

    Google Scholar 

  27. Kanis, J.A., McCloskey, E.V., Johansson, H., Oden, A., Melton III, L.J., Khaltaev, N.: A reference standard for the description of osteoporosis. Bone 42(3), 467–475 (2008)

    Article  Google Scholar 

  28. McInerney, T., Terzopoulos, D.: Deformable models in medical image analysis: a survey. Med. Image Anal. 1(2), 91–108 (1996)

    Google Scholar 

  29. Southard, T.E. and Southard, K.A.: Detection of simulated osteoporosis in maxillae using radiographic texture analysis. IEEE Trans. Biomed. Eng. 43(2), 123–132 (1996)

    Google Scholar 

  30. Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)

    Google Scholar 

  31. Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw 61, 85–117 (2015)

    Article  Google Scholar 

  32. Hinton, G.E., Osindero, S., Teh, Y.W.: A fast learning algorithm for deep beliefnets. Neural Comput. 18(7), 1527–1554 (2006)

    Article  MathSciNet  Google Scholar 

  33. Badea, M.S., Felea, I.I., Florea, L.M., Vertan, C.: The use of deep learning in image segmentation, classification, and detection. In: arXiv preprint arXiv:1605.09612 (2016)

    Google Scholar 

  34. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436(2015)

    Google Scholar 

  35. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)

    Google Scholar 

  36. Han, J., Zhang, D., Wen, S., Guo, L., Liu, T., Li, X.: Two-stage learning to predict human eye fixations via SDAEs. IEEE Trans. Cybern. 46(2), 487–498 (2016)

    Article  Google Scholar 

  37. Jensen, J.E., Sharpe, P.K., Caleb, P., Sørensen, H.A.: Fracture prediction using artificial neural networks. Osteoporos. Int. 6, 132 (1996)

    Article  Google Scholar 

  38. Redei, J., Ouyang, X., Countryman, P.J., Wang, X., Genant, H.K.: Classification of vertebral fractures: landmark-based shape recognition by neural networks. Osteoporos. Int. 6, 126 (1996)

    Article  Google Scholar 

  39. Rae, S.A., Wang, W.J., Partridge, D.: Artificial neural networks: a potential role in osteoporosis. J. R. Soc. Med. 92(3), 119–122 (1999)

    Article  Google Scholar 

  40. Liu, Q., Cui, X., Chou, Y.C., Abbod, M.F., Lin, J., Shieh, J.S.: Ensemble artificial neural networks applied to predict the key risk factors of hip bone fracture for elders. Biomed. Signal Process. Control 21, 146–156 (2015)

    Article  Google Scholar 

  41. Mohamed, E.I., Maiolo, C., Linder, R., Pöppl, S.J., De Lorenzo, A.: Artificial neural network analysis: a novel application for predicting site-specific bone mineral density. Acta Diabetol. 40(1), s19–s22 (2003)

    Article  Google Scholar 

  42. Sadatsafavi, M., Moayyeri, A., Soltani, A., Larijani, B., Nouraie, M., Akhondzadeh, S.: Artificial neural networks in prediction of bone density among post-menopausal women. J. Endocrinol. Invest. 28(7), 425–431 (2005)

    Article  Google Scholar 

  43. Chiu, J.S., Li, Y.C., Yu, F.C. and Wang, Y.F.: Applying an artificial neural network to predict osteoporosis in the elderly. In: Studies in Health Technology and Informatics, vol. 124, p. 609 (2006)

    Google Scholar 

  44. Abdel-Mageed, S.M., Bayoumi, A.M., Mohamed, E.I.: Artificial neural networks analysis for estimating bone mineral density in an Egyptian population: towards standardization of DXA measurements. Am. J. Neural Netw. Appl. 1(3), 52–56 (2015)

    Google Scholar 

  45. de Cos Juez, F.J., Suárez-Suárez, M.A., Lasheras, F.S., Murcia-Mazón, A.: Application of neural networks to the study of the influence of diet and lifestyle on the value of bone mineral density in post-menopausal women. Math. Comput. Model. 54(7–8), 1665–1670 (2011)

    Google Scholar 

  46. Shaikh, A.B., Sarim, M., Raffat, S.K., Ahsan, K., Nadeem, A., Siddiq, M.: Artificial neural network: a tool for diagnosing osteoporosis. Res. J. Recent Sci. ISSN 2277, 2502 (2014)

    Google Scholar 

  47. Bortone, I., Trotta, G.F., Cascarano, G.D., Regina, P., Brunetti, A., De Feudis, I., Buongiorno, D., Loconsole, C., Bevilacqua, V.: A Supervised approach to classify the status of bone mineral density in post-menopausal women through static and dynamic baropodometry. In: International Joint Conference on Neural Networks (IJCNN) 2018, pp. 1–7. IEEE (2018)

    Google Scholar 

  48. Iliou, T., Anagnostopoulos, C.N., Stephanakis, I.M., Anastassopoulos, G.: A novel data preprocessing method for boosting neural network performance: a case study in osteoporosis prediction. Inf. Sci. 380, 92–100 (2017)

    Article  Google Scholar 

  49. Akgundogdu, A., Jennane, R., Aufort, G., Benhamou, C.L., Ucan, O.N.: 3D image analysis and artificial intelligence for bone disease classification. J. Med. Syst. 34(5), 815–828 (2010)

    Article  Google Scholar 

  50. Taber’s online homepage, https://www.tabers.com/tabersonline/repview?type=539-126&name=o120p0

  51. Aufort, G., Jennane, R., Harba, R. and Benhamou, C.L.: Hybrid skeleton graph analysis of disordered porous media. Application to trabecular bone. In: 2006 IEEE International Conference on Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings, vol. 2, pp. II–II. IEEE (2006)

    Google Scholar 

  52. Lee, J.H., Hwang, Y.N., Park, S.Y. and Kim, S.M.: Diagnosis of osteoporosis by quantification of trabecular microarchitectures from hip radiographs using artificial neural networks. In: Bio-Inspired Computing-Theories and Applications, pp. 247–250. Springer, Berlin, Heidelberg (2014)

    Google Scholar 

  53. Vishnu, T., Saranya, K., Arunkumar, R., Gayathri Devi, M.: Efficient and early detection of osteoporosis using trabecular region. In 2015 Online International Conference on Green Engineering and Technologies (IC-GET), pp. 1–5. IEEE (2015)

    Google Scholar 

  54. Yu, X., Ye, C., Xiang, L.: Application of artificial neural network in the diagnostic system of osteoporosis. Neurocomputing 214, 376–381 (2016)

    Article  Google Scholar 

  55. Singh, A., Dutta, M.K., Jennane, R., Lespessailles, E.: Classification of the trabecular bone structure of osteoporotic patients using machine vision. Comput. Biol. Med. 91, 148–158 (2017)

    Article  Google Scholar 

  56. Mohamed, E.I., Meshref, R.A., Abdel Mageed, S.M., Moustafa, M.H., Badawi, M.I., Darwish, S.H.: A novel morphological analysis of DXA-DICOM images by artificial neural networks for estimating bone mineral density in health and disease. J. Clin. Densitometry (2018)

    Google Scholar 

  57. Areeckal, A.S., Jayasheelan, N., Kamath, J., Zawadynski, S., Kocher, M.: Early diagnosis of osteoporosis using radiogrammetry and texture analysis from hand and wrist radiographs in Indian population. Osteoporos. Int. 29(3), 665–673 (2018)

    Article  Google Scholar 

  58. Hatano, K., Murakami, S., Lu, H., Tan, J.K., Kim, H., Aoki, T.: Classification of osteoporosis from phalanges CR images based on DCNN. In: 2017 17th International Conference on Control, Automation, and Systems (ICCAS), pp. 1593–1596. IEEE (2017)

    Google Scholar 

  59. Tomita, N., Cheung, Y.Y., Hassanpour, S.: Deep neural networks for automatic detection of osteoporotic vertebral fractures on CT scans. Comput. Biol. Med. 98, 8–15 (2018)

    Article  Google Scholar 

  60. Lee, J.S., Adhikari, S., Liu, L., Jeong, H.G., Kim, H. and Yoon, S.J.: Osteoporosis detection in panoramic radiographs using a deep convolutional neural network-based computer-assisted diagnosis system: a preliminary study. Dentomaxillofacial Radiol. 48(1), 20170344 (2019)

    Google Scholar 

  61. Deniz, C.M., Xiang, S., Hallyburton, R.S., Welbeck, A., Babb, J.S., Honig, S., Cho, K., Chang, G.: Segmentation of the proximal femur from MR images using deep convolutional neural networks. Sci. Rep. 8(1), 16485 (2018)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer Science Engineering, Shri Mata Vaishno Devi University, Katra, Jammu and Kashmir, India

    Insha Majeed Wani & Sakshi Arora

Authors
  1. Insha Majeed Wani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sakshi Arora
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sakshi Arora .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Jaypee University of Information Technology, Waknaghat, Himachal Pradesh, India

    Pradeep Kumar Singh

  2. Indian Institute of Technology Delhi, New Delhi, Delhi, India

    Arpan Kumar Kar

  3. Central University of Jammu, Jammu, Jammu and Kashmir, India

    Yashwant Singh

  4. Indian Institute of Technology Patna, Patna, Bihar, India

    Maheshkumar H. Kolekar

  5. Institute of Technology, Nirma University, Ahmedabad, Gujarat, India

    Sudeep Tanwar

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wani, I.M., Arora, S. (2020). Deep Neural Networks for Diagnosis of Osteoporosis: A Review. In: Singh, P., Kar, A., Singh, Y., Kolekar, M., Tanwar, S. (eds) Proceedings of ICRIC 2019 . Lecture Notes in Electrical Engineering, vol 597. Springer, Cham. https://doi.org/10.1007/978-3-030-29407-6_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-29407-6_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-29406-9

  • Online ISBN: 978-3-030-29407-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation