Deep Neural Networks for Diagnosis of Osteoporosis: A Review

  • Insha Majeed Wani
  • Sakshi AroraEmail author
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 597)


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.


Osteoporosis DXA Neural networks Deep learning 


  1. 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. 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)CrossRefGoogle Scholar
  3. 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. 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. 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)CrossRefGoogle Scholar
  6. 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)CrossRefGoogle Scholar
  7. 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)CrossRefGoogle Scholar
  8. 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. 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. 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. 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. 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. 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. 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)CrossRefGoogle Scholar
  15. 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. 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. 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. 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. 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. 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)CrossRefGoogle Scholar
  21. 21.
    Osteoporosis. Park Ridge, IL, American Academy of Orthopaedic Surgeons, 1986Google Scholar
  22. 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. 23.
    Kaplan, F.S.: Osteoporosis. Women’s Health 10(2/3), 95–114 (1985)Google Scholar
  24. 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. 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. 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. 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)CrossRefGoogle Scholar
  28. 28.
    McInerney, T., Terzopoulos, D.: Deformable models in medical image analysis: a survey. Med. Image Anal. 1(2), 91–108 (1996)Google Scholar
  29. 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. 30.
    Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)Google Scholar
  31. 31.
    Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw 61, 85–117 (2015)CrossRefGoogle Scholar
  32. 32.
    Hinton, G.E., Osindero, S., Teh, Y.W.: A fast learning algorithm for deep beliefnets. Neural Comput. 18(7), 1527–1554 (2006)MathSciNetCrossRefGoogle Scholar
  33. 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. 34.
    LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436(2015)Google Scholar
  35. 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. 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)CrossRefGoogle Scholar
  37. 37.
    Jensen, J.E., Sharpe, P.K., Caleb, P., Sørensen, H.A.: Fracture prediction using artificial neural networks. Osteoporos. Int. 6, 132 (1996)CrossRefGoogle Scholar
  38. 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)CrossRefGoogle Scholar
  39. 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)CrossRefGoogle Scholar
  40. 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)CrossRefGoogle Scholar
  41. 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)CrossRefGoogle Scholar
  42. 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)CrossRefGoogle Scholar
  43. 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. 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. 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. 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. 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. 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)CrossRefGoogle Scholar
  49. 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)CrossRefGoogle Scholar
  50. 50.
  51. 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. 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. 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. 54.
    Yu, X., Ye, C., Xiang, L.: Application of artificial neural network in the diagnostic system of osteoporosis. Neurocomputing 214, 376–381 (2016)CrossRefGoogle Scholar
  55. 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)CrossRefGoogle Scholar
  56. 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. 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)CrossRefGoogle Scholar
  58. 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. 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)CrossRefGoogle Scholar
  60. 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. 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)CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.School of Computer Science EngineeringShri Mata Vaishno Devi UniversityKatraIndia

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