A Framework on a Computer Assisted and Systematic Methodology for Detection of Chronic Lower Back Pain Using Artificial Intelligence and Computer Graphics Technologies

  • Ala S. Al KafriEmail author
  • Sud Sudirman
  • Abir J. Hussain
  • Paul Fergus
  • Dhiya Al-Jumeily
  • Mohammed Al-Jumaily
  • Haya Al-Askar
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9771)


Back pain is one of the major musculoskeletal pain problems that can affect many people and is considered as one of the main causes of disability all over the world. Lower back pain, which is the most common type of back pain, is estimated to affect at least 60 % to 80 % of the adult population in the United Kingdom at some time in their lives. Some of those patients develop a more serious condition namely Chronic Lower Back Pain in which physicians must carry out a more involved diagnostic procedure to determine its cause. In most cases, this procedure involves a long and laborious task by the physicians to visually identify abnormalities from the patient’s Magnetic Resonance Images. Limited technological advances have been made in the past decades to support this process. This paper presents a comprehensive literature review on these technological advances and presents a framework of a methodology for diagnosing and predicting Chronic Lower Back Pain. This framework will combine current state-of-the-art computing technologies including those in the area of artificial intelligence, physics modelling, and computer graphics, and is argued to be able to improve the diagnosis process.


Computer aided/assisted diagnosis Chronic Lower Back Pain Artificial intelligence Physics modelling Computer graphics 


  1. 1.
    McCamey, K., Evans, P.: Low back pain. Prim. Care 34(1), 71–82 (2007)CrossRefGoogle Scholar
  2. 2.
  3. 3.
    Waddell, G.: The Back Pain Revolution, 2nd edn. Churchill Livingstone (Elsevier), Edinburgh (2004)Google Scholar
  4. 4.
    Croft, P.R., Macfarlane, G.J., Papageorgiou, A.C., Thomas, E., Silman, A.J.: Outcome of low back pain in general practice: a prospective study. Br. Med. J. 316(7141), 1356 (1998)CrossRefGoogle Scholar
  5. 5.
    Waddell, G., Burton, A.K.: Occupational health guidelines for the management of low back pain at work: evidence review. Occup. Med. (Chic. Ill) 51(2), 124–135 (2001)CrossRefGoogle Scholar
  6. 6.
    Burton, K., Kendall, N.: Musculoskeletal disorders. BMJ 348, bmj.g1076 (2014)CrossRefGoogle Scholar
  7. 7.
    Ellis, R.M.: Back pain. BMJ 310(6989), 1220 (1995)CrossRefGoogle Scholar
  8. 8.
    Raj, P., Nolte, H., Stanton-Hicks, M.: Anatomy of the spine. In: Raj, P., Klinder, T., Stanton-Hicks, M. (eds.) Illustrated Manual of Regional Anesthesia, pp. 3–7. Springer, Heidelberg (1988)Google Scholar
  9. 9.
    Proximal, P.P., Supplement, P.P., Powers, C.M., Bolgla, L.A., Callaghan, M.J., Collins, N., Sheehan, F.T.: Patellofemoral pain: proximal, distal, and local factors–2nd international research retreat. J. Orthop. Sports Phys. Ther. 42(6), 1–55 (2012)CrossRefGoogle Scholar
  10. 10.
    Methods, R., Practices, B.: Appropriateness of care: use of MRI in the investigation of patient low back pain executive summary, pp. 1–29 (2015)Google Scholar
  11. 11.
    Raja’S, A., Corso, J.J., Chaudhary, V., Dhillon, G.: Automatic diagnosis of lumbar disc herniation with shape and appearance features from MRI. In: Proceedings of SPIE Medical Imaging, p. 76241A (2010)Google Scholar
  12. 12.
    David, A.L.: 9 Spine. Imaging for students, D, pp. 187–206 (2012)Google Scholar
  13. 13.
    The Healthy Spine | Spinal Simplicity.
  14. 14.
    Lootus, M., Kadir, T., Zisserman, A.: Vertebrae detection and labelling in lumbar MR Images. In: Yao, J., Klinder, T., Li, S. (eds.) Computational Methods and Clinical Applications for Spine Imaging. Lecture Notes in Computational Vision and Biomechanics, vol. 17, pp. 219–230. Springer, Switzerland (2014)Google Scholar
  15. 15.
    Turner, J.A., Shortreed, S.M., Saunders, K.W., Leresche, L., Berlin, J.A., Von Korff, M.: Optimizing prediction of back pain outcomes. Pain 154(8), 1391–1401 (2013)CrossRefGoogle Scholar
  16. 16.
    Su, W.-C., Yeh, S.-C., Lee, S.-H., Huang, H.-C.: A virtual reality lower-back pain rehabilitation approach: system design and user acceptance analysis. In: Antona, M., Stephanidis, C. (eds.) UAHCI 2015. LNCS (LNAI and LNBI), vol. 9177, pp. 374–382. Springer, Heidelberg (2015)CrossRefGoogle Scholar
  17. 17.
    Neubert, A., Fripp, J., Engstrom, C., Schwarz, R., Lauer, L., Salvado, O., Crozier, S.: Automated detection, 3D segmentation and analysis of high resolution spine MR images using statistical shape models. Phys. Med. Biol. 57(24), 8357 (2012)CrossRefGoogle Scholar
  18. 18.
    Ahn, H.S.: A virtual model of the human cervical spine for physics-based simulation and applications. The University of Memphis (2005)Google Scholar
  19. 19.
    Nourian, S., Shen, X., Georganas, N.D.: Role of extensible physics engine in surgery simulations. In: 2005 IEEE International Workshop on Haptic Audio Visual Environments and their Applications (2005)Google Scholar
  20. 20.
    Hoad, C.L., Martel, A.L., Kerslake, R., Grevitt, M.: A 3D MRI sequence for computer assisted surgery of the lumbar spine. Phys. Med. Biol. 46(8), N213 (2001)CrossRefGoogle Scholar
  21. 21.
    Morais, S.T.: Development of a biomechanical spine model for dynamic analysis, Universidade do Minho (2011)Google Scholar
  22. 22.
    Shirazi-Adl, A., Ahmed, A.M., Shrivastava, S.C.: A finite element study of a lumbar motion segment subjected to pure sagittal plane moments. J. Biomech. 19(4), 331–350 (1986)CrossRefGoogle Scholar
  23. 23.
    Alomari, R.S., Corso, J.J., Chaudhary, V., Dhillon, G.: Lumbar spine disc herniation diagnosis with a joint shape model. Clin. Appl. Spine Imaging 17, 87–98 (2014)Google Scholar
  24. 24.
    Lisin, D.A., Mattar, M.A., Blaschko, M.B., Benfield, M.C., Learned-miller, E.G.: Combining Local and Global Image Features for Object Class Recognition. In: CVPR Workshops (2005)Google Scholar
  25. 25.
    Freidman, J.H., Bentley, J.L., Finkel, R.A.: An algorithm for finding best matches in logarithmic expected time. ACM Trans. Math. Softw. 3(3), 209–226 (1977)CrossRefzbMATHGoogle Scholar
  26. 26.
    Arya, S., Mount, D.M., Netanyahu, N.S., Silverman, R., Wu, A.Y.: An optimal algorithm for approximate nearest neighbor searching in fixed dimensions. In: Proceedings of 5th ACM-SIAM Symposium on Discrete Algorithms, vol. 1, no. 212, pp. 573–582 (1994)Google Scholar
  27. 27.
    Beis, J.S., Lowe, D.G.: Shape indexing using approximate nearest-neighbour search in high-dimensional spaces. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1000–1006 (1997)Google Scholar
  28. 28.
    Muja, M., Lowe, D.G.: Scalable nearest neighbour methods for high dimensional data. IEEE Trans. Pattern Anal. Mach. Intell. 36(11), 1–14 (2014)CrossRefGoogle Scholar
  29. 29.
    Alomari, R.S., Corso, J.J., Chaudhary, V., Dhillon, G.: Lumbar spine disc herniation diagnosis with a joint shape model. In: Yao, J., Klinder, T., Li, S. (eds.) Computational Methods and Clinical Applications for Spine Imaging. Lecture Notes in Computational Vision and Biomechanics, vol. 17, pp. 87–98. Springer, Switzerland (2014)Google Scholar
  30. 30.
    Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. Pattern Anal. Mach. Intell. IEEE Trans. 32(9), 1627–1645 (2010)CrossRefGoogle Scholar
  31. 31.
    Pedro, D.P.H., Felzenszwalb, F.: Pictorial structures for object recognition. Int. J. Comput. Vis. 61, 55–79 (2004)Google Scholar
  32. 32.
    Koh, J., Alomari, R.S., Dhillon, G.: Lumbar spinal stenosis CAD from clinical MRM and MRI based on inter-and intra-context Features with a two-level classifier 7963, 796304–796308 (2011)Google Scholar
  33. 33.
    Kamogawa, J., Kato, O.: Virtual Anatomy of Spinal Disorders by 3-D MRI/CT Fusion Imaging (2010). (no. Table 1)Google Scholar
  34. 34.
    Huynh, K., Gibson, I., Gao, Z.: Development of a Detailed Human Spine Model with Haptic Interface, pp. 165–195 (2012).
  35. 35.
    Lavaste, F., Skalli, W., Robin, S., Roy-Camille, R., Mazel, C.: Three-dimensional geometrical and mechanical modelling of the lumbar spine. J. Biomech. 25(10), 1153–1164 (1992)CrossRefGoogle Scholar
  36. 36.
    Nabhani, F., Wake, M.: Computer modelling and stress analysis of the lumbar spine. J. Mater. Process. Technol. 127(1), 40–47 (2002)CrossRefGoogle Scholar
  37. 37.
    Noailly, J., Wilke, H.-J., Planell, J.A., Lacroix, D.: How does the geometry affect the internal biomechanics of a lumbar spine bi-segment finite element model? Consequences on the validation process. J. Biomech. 40(11), 2414–2425 (2007)CrossRefGoogle Scholar
  38. 38.
    Feipel, V., De Mesmaeker, T., Klein, P., Rooze, M.: Three-dimensional kinematics of the lumbar spine during treadmill walking at different speeds. Eur. Spine J. 10(1), 16–22 (2001)CrossRefGoogle Scholar
  39. 39.
    Papadakis, N.C., Christakis, D.G., Tzagarakis, G.N., Chlouverakis, G.I., Kampanis, N.A., Stergiopoulos, K.N., Katonis, P.G.: Gait variability measurements in lumbar spinal stenosis patients: part A. Comparison with healthy subjects. Physiol. Meas. 30(11), 1171–1186 (2009)CrossRefGoogle Scholar
  40. 40.
    Kulig, K., Landel, R.F., Powers, C.M.: Assessment of lumbar spine kinematics using dynamic MRI: a proposed mechanism of sagittal plane motion induced by manual posterior-to-anterior mobilization. J. Orthop. Sport. Phys. Ther. 34(2), 57–64 (2004)CrossRefGoogle Scholar
  41. 41.
    Marks, S., Windsor, J., Wünsche, B.: Evaluation of game engines for simulated clinical training. In: New Zealand Computer Science Research Student Conference (NZCSRSC) (2008)Google Scholar
  42. 42.
    Alomari, R.S., Corso, J.J., Chaudhary, V., Dhillon, G.: Automatic diagnosis of lumbar disc herniation with shape and appearance features from MRI. Prog. Biomed. Opt. Imaging 11, 76241A (2010)Google Scholar
  43. 43.
    Ghosh, S., Alomari, R.S., Chaudhary, V., Dhillon, G.: Computer-aided diagnosis for lumbar MRI using heterogeneous classifiers. In: Proceedings of International Symposium Biomedical Imaging, pp. 1179–1182 (2011)Google Scholar
  44. 44.
    Jordan, J., Konstantinou, K., O’Dowd, J.: Herniated lumbar disc. BMJ Clin. Evid. (2009)Google Scholar
  45. 45.
    Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of 2001 IEEE Conference on Computer Vision and Pattern Recognition CVPR 2001, vol. 1, C, pp. 511–518 (2001)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Ala S. Al Kafri
    • 1
    Email author
  • Sud Sudirman
    • 1
  • Abir J. Hussain
    • 1
  • Paul Fergus
    • 1
  • Dhiya Al-Jumeily
    • 1
  • Mohammed Al-Jumaily
    • 2
  • Haya Al-Askar
    • 3
  1. 1.Faculty of Engineering and TechnologyLiverpool John Moores UniversityLiverpoolUK
  2. 2.Consultant Neurosurgeon and Spine SurgeonDr Sulaiman Al Habib Hospital, Dubai Healthcare CityDubaiUAE
  3. 3.College of Computer Engineering and ScienceSattam Bin Abdulaziz UniversityAl-KharjKingdom of Saudi Arabia

Personalised recommendations