Skip to main content

Abstract

Virtual Reality (VR) technology is widely used in scientific, engineering and educational applications all over the world. The technology is also widely advancing day by day, but, the applications in medical fields are limited. Medical technology is one of the most advancing technologies which are evolving due to unlimited need of health requirement. Further, Computational Intelligence (CI) contributed much promising aspects of many healthcare practices such as treatment, disease diagnosis, direct follow-ups, rehabilitation setups, preventive measures and administrative management practices etc. Dental sciences have witnessed many developments. In many ways, VR based surgery practices are governed by computer assistance. The conjunction of these two technological aspects to a larger extent can solve various issues in modern healthcare systems. With the introduction of newer healthcare technology, the medical issues nevertheless happen to be overcome. Nevertheless the scope in this kind of study is boundless.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 179.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 179.99
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. Stanney K. M. (2000). Handbook of virtual environments. In K. M. Stanneyed (Ed.), Handbook of virtual environments: Design, implementation and applications (pp. 301–302). Mahwah NJ. Lawrence Erlbaum Associates, Inc.

    Google Scholar 

  2. Pulijala, Y., Ma, M., Pears, M., Peebles, D., & Ayoub, A. (2018). An innovative virtual reality training tool for orthognathic surgery. International Journal of Oral and Maxillofacial Surgery, 47(9), 1199–1205.

    Google Scholar 

  3. Sik Lanyi, C. (2006). Virtual reality in healthcare, intelligent paradigms for assistive and preventive healthcare. In A. Ichalkaranje, et al. (Eds.), (pp. 92–121). Berlin: Springer. https://doi.org/10.3109/02699052.2016.1144146.

    Article  Google Scholar 

  4. Yates, M., Kelemen, A., & Sik-Lanyi, C. (2016). Virtual reality gaming in the rehabilitation of the upper extremities post-stroke. Brain Injury, 30(7), 855–863.

    Google Scholar 

  5. Tagaytaya, R., A. Kelemen, A., & Sik-Lanyi, C. (2016). Augmented reality in neurosurgery. Archive of Medical Science. https://doi.org/10.5114/aoms.2016.58690. Published online: 22 March 2016.

    Article  Google Scholar 

  6. Mazur, T., Mansour, T. R., Mugge, L., & Medhkour, A. (2018). Virtual reality–Based simulators for cranial tumor surgery: A systematic review. World Neurosurgery, 110, 414–422.

    Google Scholar 

  7. Tractica from https://www.tractica.com/wpcontent/uploads/2015/09/VREI-15-Brochure.pdf. Last Accessed September 27, 2018.

  8. Medical Realities http://www.medicalrealities.com. Last Accessed September 26, 2018.

  9. VR healthnet http://healthnet.com. Last Accessed September 26, 2017.

  10. Chada, B. V. (2017). Virtual consultations in general practice: embracing innovation, carefully. British Journal of General Practice, 264.

    Article  Google Scholar 

  11. Kaffash, J. (2017, June 10). Average waiting time for GP appointment increases 30% in a year. Pulse 2016. http://www.pulsetoday.co.uk/yourpractice/access/average-waiting-timefor-gpappointment-increases-30-in-a-year/20032025. Last Accessed April 25, 2018.

  12. Greenhalgh, T., Vijayaraghavan, S., Wherton, J., et al. (2016). Virtual on-line consultations: advantages and limitations (VOCAL) stud. British Medical Journal Open, 6(1), e009388.

    Article  Google Scholar 

  13. WHO. (2011). mHealth New horizons for health through mobile technologies, Global Observatory for eHealth series—Volume 3, WHO library cataloguing-in-publication data. http://www.who.int/goe/publications/goe_mhealth_web.pdf. Last Accessed October 4, 2017.

  14. Yountae, L., & Hyejung, C. (2012). Ubiquitous health in Korea: Progress, barriers, and prospects. Healthcare Informatics Research, 18(4), 242–251. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3548153/#B13. Last Accessed October 4, 2018.

  15. Kang, S. W., Lee, S. H., & Koh, Y. S. (2007). Emergence of u-Health era. CEO Inf (602), 1–4.

    Google Scholar 

  16. Ganrya, L., Hersanta, B., Sidahmed-Mezia, M., Dhonneurb, G., & Meningauda, J. P. (2018). Using virtual reality to control preoperative anxiety in ambulatory surgery patients: A pilot study in maxillofacial and plastic surgery. Journal of Stomatology, Oral and Maxillofacial Surgery, 119(4), 257–261.

    Google Scholar 

  17. Quero, G., Lapergola, A., Soler, L., Shabaz, M., Hostettler, A., Collins T, et al. (2019). Virtual and augmented reality in oncologic liver surgery. Surgical Oncology Clinics of North America, 28(1), 31–44.

    Article  Google Scholar 

  18. Qiumingguo, & Shaoxiang, Z. (2013). Development of applications is boundless. Computer World, 2003.

    Google Scholar 

  19. Tanglei. (2001). Virtual surgery. In http://www.sungraph.com.cn/, July 2001.

  20. Hua, Q. (2004). The applications of VR in medicine. In http://www.86vr.com/apply, October 2004.

  21. Vince, J. (2002). Virtual reality systems. Boston: Addison Wesley Publishing.

    Google Scholar 

  22. Zajac, F. R., & Delp, S. L. (1992). Force and moment generating capacity of lower limb muscles before and after tendon lengthening. Clinical Orthopaedic Related Research, 284, 247–259.

    Google Scholar 

  23. Satava, R. M. (1993). Virtual reality surgical simulator: The first steps. In Surgical endoscopy (vol. 7, pp. 203–205).

    Article  Google Scholar 

  24. Merril, J. R., Merril, G. L., Raju, R., Millman, A., Meglan, D., Preminger, G. M., et al. (1995). Photorealistic interactive three-dimensional graphics in surgical simulation. In R. M. Satava, K. S. Morgan, H. B. Sieburg, R. Masttheus, & J. P. Christensen (Eds.), Interactive technology and the new paradigm for healthcare (pp. 244–252). Washington, DC: IOS Press.

    Google Scholar 

  25. Spitzer, V. M., & Whitlock, D. G. (1992). Electronic imaging of the human body. Data storage and interchange format standards. In M. W. Vannier, R. E. Yates, & J. J. Whitestone (Eds.), Proceedings of Electronic Imaging of the Human Body Working Group (pp. 66–68).

    Google Scholar 

  26. Meglan, D. A., Raju, R., Merril, G. L., Merril, J. R., Nguyen, B. H., Swamy, S. N., & Higgins, G. A. (1995). Teleos virtual environment for simulation-based surgical education. In R. M. Satava, K. S. Morgan, H. B. Sieburg, R. Masttheus, & J. P. Christensen (Eds.), Interactive technology and the new paradigm for healthcare (pp. 346–351). Washington, DC: IOS Press.

    Google Scholar 

  27. Raibert, M. A. Personal communication.

    Google Scholar 

  28. Lorensen, W. E., Jolesz, F. A., & Kikinis, R. (1995). The exploration of cross-sectional data with a virtual endoscope, In R. M. Satava, K. S. Morgan, H. B. Sieburg, R. Masttheus, & J. P. Christensen (Eds.), Interactive technology and the new paradigm for healthcare (pp. 221–230). Washington, DC: IOS Press.

    Google Scholar 

  29. Geiger, B., & Kikinis, R. (1994). Simulation of endoscopy. In Proceedings of AAAI Spring Symposium Series: Applications of Computer Vision in Medical Images Processing (pp. 138–140). Stanford: Stanford University.

    Google Scholar 

  30. Buchanan, B. G., & Shortliffe, E. H. (1984). Rule-based expert systems. In The Mycin experiments of the Stanford heuristic programming project. Boston: Addison-Wesley.

    Google Scholar 

  31. Heckerman, D. E., Horvitz, E. J., & Nathwani, B. N. (1992). Toward normative expert systems: The pathfinder project. Methods of Information in Medicine, 31(2), 90–105.

    Google Scholar 

  32. Kang, K. W. (2012). Feasibility of an automatic computer-assisted algorithm for the detection of significant coronary artery disease in patients presenting with acute chest pain. European Journal Radiology, 81(4), 640–646.

    Article  Google Scholar 

  33. Zhang, Y., & Szolovits, P. (2008). Patient specific learning in real time for adaptive monitoring in critical care. Journal of Biomedical Informatics, 41(3), 452–460.

    Google Scholar 

  34. Saria, S. (2010). Integration of early physiological responses predicts later illness severity in preterm infants. Science Translational Medicine, 2(48), 48–65.

    Article  Google Scholar 

  35. Wiens, J., Guttag, J. V., & Horvitz, E. (2012). Patient risk stratification for hospital associated c. diff as a time-series classification task. In Advances in neural information systems, neural information processing systems (NIPS) Foundation (Vol. 25, pp. 247–255).

    Google Scholar 

  36. Levin, S. R. (2012). Real-time forecasting of pediatric intensive care unit length of stay using computerized provider orders. Critical Care Medicine, 40(11), 3058–3064.

    Article  Google Scholar 

  37. Ferrucci, D. (2010). Building Watson: An overview of the Deep QA project. AI Magazine, 31(3), 59–79.

    Article  Google Scholar 

  38. Kohn, M. S., & Skarulis, P. C. (2012). IBM Watson delivers new insights for treatment and diagnosis. In Digital Health Conference, Presentation.

    Google Scholar 

  39. Lenat, D. (2010). Cyc to answer clinical researchers’ Ad Hoc Queries. AI Magazine, 31(3), 13–32.

    Article  Google Scholar 

  40. Tsuda, S., Scott, D., Doyle, J., & Jones, D. B. (2009). Surgical skills training and simulation. Current Problem in Surgery, 46(4), 271–370.

    Google Scholar 

  41. Forestier, G., Petitjean, F., Riffaud, L., & Jannin, P. (2015). Optimal sub-sequence matching for the automatic prediction of surgical tasks. In AIME 15th Conference on Artificial Intelligence in Medicine, 9105 (pp. 123–32).

    Google Scholar 

  42. Forestier, G., Petitjean, F., Riffaud, L., & Jannin, P. (2017). Automatic matching of surgeries to predict surgeons’ next actions. Artificial Intelligence Medicine, 2017(81), 3–11.

    Article  Google Scholar 

  43. Dlouhy, B. J., & Rao, R. C. (2014). Surgical skill and complication rates after bariatric surgery. England Journal of Medicine, 370(3), 285.

    Google Scholar 

  44. Maier-Hein, L., Vedula, S. S., Speidel, S., Navab, N., Kikinis, R., & Park, A. (2017). Surgical data science for next-generation interventions. National Biomedical Engineering, 1(9), 691.

    Google Scholar 

  45. Shafiei, S. B., Cavuoto, L., & Guru, K. A. (2017). Motor skill evaluation during robot-assisted surgery. In International Design Engineering Technical Conferences & Computers and Information in Engineering Conference IDETC/CIE 2017. Cleveland, Ohio, USA.

    Google Scholar 

  46. Sharon, Y., & Nisky, I. (2017). What can spatiotemporal characteristics of movements in RAMIS tell us? ArXiv e-prints 2017.

    Google Scholar 

  47. Li, K., & Burdick, J. W. (2017). A function approximation method for model-based high-dimensional inverse reinforcement learning. ArXiv e-prints:1708.07738.

    Google Scholar 

  48. Marban, A., Srinivasan, V., Samek, W., Fernandez, J., & Casals, A. (2017). Estimating position & velocity in 3d space from monocular video sequences using a deep neural network. In The IEEE International Conference on Computer Vision (ICCV) 2017.

    Google Scholar 

  49. Rupprecht, C., Lea, C., Tombari, F., Navab, N., & Hager, G. D. (2016). Sensor substitution for video based action recognition. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2016, 5230–5237.

    Article  Google Scholar 

  50. Sarikaya, D., Corso, J. J., & Guru, K. A. (2017). Detection and localization of robotic tools in robot assisted surgery videos using deep neural networks for region proposal and detection. IEEE Transactions on Medical Imaging, 36(7), 1542–1549.

    Google Scholar 

  51. Fard, M. J., Pandya, A. K., Chinnam, R. B., Klein, M. D., & Ellis, R. D. (2017). Distance-based time series classification approach for task recognition with application in surgical robot autonomy. International Journal Med Robot Comput Assist Surgery, 13(3). e1766-n/a. E1766 RCS-16-0026.R2.

    Google Scholar 

  52. Bani, M. J., & Jamali, S. (2017). A new classification approach for robotic surgical tasks recognition. ArXiv e-prints:1707.09849.

    Google Scholar 

  53. Ahmidi, N., Tao, L., Sefati, S., Gao, Y., Lea, C., & Bejar, B. (2017). A dataset and benchmarks for segmentation and recognition of gestures in robotic surgery. IEEE Transactions Biomedical Engineering.

    Google Scholar 

  54. Alemdar, H., & Ersoy, C. (2010). Wireless sensor networks for healthcare: A survey. Computer Network, 54(15), 2688–2710.

    Google Scholar 

  55. Esposito, A., Esposito, A. M., Likforman-Sulem, L., Maldonato, M. N., & Vinciarelli, A. (2016). On the significance of speech pauses in depressive disorders: results on read and spontaneous narratives. In Recent advances in nonlinear speech processing (pp. 73–82). Berlin: Springer.

    Google Scholar 

  56. Tsanas, A., Little, M. A., McSharry, P. E., & Ramig, L. O. (2010). Accurate tele-monitoring of Parkinson’s disease progression by noninvasive speech tests. IEEE Transactions Biomedical Engineering, 57(4), 884–893.

    Google Scholar 

  57. Virtual and augmented reality software revenue from https://www.statista.com/chart/4602/virtual-and-augmented-realitysoftware-revenue/. Last Accessed September 27, 2018.

  58. Gao, Y., Vedula, S. S., Reiley, C. E., Ahmidi, N., Varadarajan, B., & Lin, H. C. (2014). JHU-ISI gesture and skill assessment working set (JIGSAWS): A surgical activity dataset for human motion modeling. In Modeling and Monitoring of Computer Assisted Interventions (M2CAI)-MICCAI Workshop (pp. 1–10).

    Google Scholar 

  59. Despinoy, F., Bouget, D., Forestier, G., Penet, C., Zemiti, N., & Poignet, P. (2016). Unsupervised trajectory segmentation for surgical gesture recognition in robotic training. IEEE Transactions of Biomedical Engineering, 2016, 1280–1291.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ambarish G. Mohapatra .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Keswani, B. et al. (2020). World of Virtual Reality (VR) in Healthcare. In: Gupta, D., Hassanien, A., Khanna, A. (eds) Advanced Computational Intelligence Techniques for Virtual Reality in Healthcare. Studies in Computational Intelligence, vol 875. Springer, Cham. https://doi.org/10.1007/978-3-030-35252-3_1

Download citation

Publish with us

Policies and ethics