Skip to main content

Future Perspective of Augmented Reality in Minimally Invasive Spine Surgery

  • Chapter
  • First Online:
Technical Advances in Minimally Invasive Spine Surgery

Abstract

The ideal solution for navigation in MISS is not necessarily one that is easily sorted into a single category. Even today, AR technology is combined with other technologies to create hybrids of AR, VR, and MixR technologies. Future solutions should focus on solving the remaining problems and challenges faced in the treatment of spine pathologies. However, tailoring solutions to every possible problem the surgeon may encounter can also create a problem by providing the surgeon with too many tools and options. Instead, a simplified workflow should be pursued in which technology follows the necessary steps of a given surgery. Note, however, that these steps may change with the continued evolution of technical solutions for safe and efficient surgery. Although the technical accuracy of different AR solutions is already relatively high, pushing the boundaries of what is technically possible must remain a priority and serve as a prerequisite for the continued development of the surgical field. However, consistency must also be prioritized, yielding systems that allow less experienced surgeons to perform accurate and safe surgeries every time. Inbuilt safeguards must protect the surgeon from making mistakes related to a loss of navigational accuracy or unseen deflections and errors of surgical instruments. Moreover, future challenges of AR also encompass achieving visualization methods that allow maximal freedom in the surgical field, including surgery performed under the microscope, providing relevant visual information without the risk of inattentional blindness.

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 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 129.99
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. Dixon BJ, Daly MJ, Chan HH, Vescan A, Witterick IJ, Irish JC. Inattentional blindness increased with augmented reality surgical navigation. Am J Rhinol Allergy. 2014;28(5):433–7.

    Article  Google Scholar 

  2. Manni F, Elmi-Terander A, Burström G, Persson O, Edström E, Holthuizen R, et al. Towards optical imaging for spine tracking without markers in navigated spine surgery. Sensors (Basel). 2020;20(13).

    Google Scholar 

  3. Manni F, van der Sommen F, Zinger S, Shan CF, Holthuizen R, Lai M, et al. Hyperspectral imaging for skin feature detection: advances in markerless tracking for spine surgery. Appl Sci-Basel. 2020;10(12)

    Google Scholar 

  4. Yeh M, Wickens CD. Display signaling in augmented reality: effects of cue reliability and image realism on attention allocation and trust calibration. Hum Factors. 2001;43(3):355–65.

    Article  CAS  Google Scholar 

  5. Edström E, Burström G, Nachabe R, Gerdhem P, Elmi-Terander A. A novel augmented-reality-based surgical navigation system for spine surgery in a hybrid operating room: design, workflow, and clinical applications. Oper Neurosurg (Hagerstown). 2020;18(5):496–502.

    Article  Google Scholar 

  6. Hartl R, Lam KS, Wang J, Korge A, Kandziora F, Audige L. Worldwide survey on the use of navigation in spine surgery. World Neurosurg. 2013;79(1):162–72.

    Article  Google Scholar 

  7. Otake Y, Schafer S, Stayman JW, Zbijewski W, Kleinszig G, Graumann R, et al. Automatic localization of vertebral levels in x-ray fluoroscopy using 3D-2D registration: a tool to reduce wrong-site surgery. Phys Med Biol. 2012;57(17):5485–508.

    Article  CAS  Google Scholar 

  8. Kim Y, Kim D. A fully automatic vertebra segmentation method using 3D deformable fences. Comput Med Imaging Graph. 2009;33(5):343–52.

    Article  Google Scholar 

  9. Klinder T, Ostermann J, Ehm M, Franz A, Kneser R, Lorenz C. Automated model-based vertebra detection, identification, and segmentation in CT images. Med Image Anal. 2009;13(3):471–82.

    Article  Google Scholar 

  10. Huang J, Jian F, Wu H, Li H. An improved level set method for vertebra CT image segmentation. Biomed Eng Online. 2013;12(48)

    Google Scholar 

  11. Mandell JG, Langelaan JW, Webb AG, Schiff SJ. Volumetric brain analysis in neurosurgery: part 1. Particle filter segmentation of brain and cerebrospinal fluid growth dynamics from MRI and CT images. J Neurosurg Pediatr. 2015;15(2):113–24.

    Article  Google Scholar 

  12. Byrnes TJ, Barrick TR, Bell BA, Clark CA. Semiautomatic tractography: motor pathway segmentation in patients with intracranial vascular malformations. Clinical article J Neurosurg 2009;111(1):132–140.

    Google Scholar 

  13. Goerres J, Uneri A, De Silva T, Ketcha M, Reaungamornrat S, Jacobson M, et al. Spinal pedicle screw planning using deformable atlas registration. Phys Med Biol. 2017;62(7):2871–91.

    Article  CAS  Google Scholar 

  14. Burström G, Buerger C, Hoppenbrouwers J, Nachabe R, Lorenz C, Babic D, et al. Machine learning for automated 3-dimensional segmentation of the spine and suggested placement of pedicle screws based on intraoperative cone beam computer tomography. J Neurosurg Spine. 2019;31(1):147–54.

    Article  Google Scholar 

  15. Ungi T, Greer H, Sunderland K, Wu V, Baum ZM, Schlenger C, et al. Automatic spine ultrasound segmentation for scoliosis visualization and measurement. 2020.

    Google Scholar 

  16. Burström G, Balicki M, Patriciu A, Kyne S, Popovic A, Holthuizen R, et al. Feasibility and accuracy of a robotic guidance system for navigated spine surgery in a hybrid operating room: a cadaver study. Sci Rep. 2020;10(1):7522.

    Article  Google Scholar 

  17. Lai M, Skyrman S, Shan C, Babic D, Homan R, Edstrom E, et al. Fusion of augmented reality imaging with the endoscopic view for endonasal skull base surgery; a novel application for surgical navigation based on intraoperative cone beam computed tomography and optical tracking. PLoS One. 2020;15(1):e0227312.

    Article  CAS  Google Scholar 

  18. Pireau N, Cordemans V, Banse X, Irda N, Lichtherte S, Kaminski L. Radiation dose reduction in thoracic and lumbar spine instrumentation using navigation based on an intraoperative cone beam CT imaging system: a prospective randomized clinical trial. Eur Spine J. 2017;26(11):2818–27.

    Article  Google Scholar 

  19. Houten JK, Nasser R, Baxi N. Clinical assessment of percutaneous lumbar pedicle screw placement using the O-arm multidimensional surgical imaging system. Neurosurgery. 2012;70(4):990–5.

    Article  Google Scholar 

  20. Uehara M, Takahashi J, Ikegami S, Kuraishi S, Shimizu M, Futatsugi T, et al. Are pedicle screw perforation rates influenced by distance from the reference frame in multilevel registration using a computed tomography-based navigation system in the setting of scoliosis? Spine J. 2017;17(4):499–504.

    Article  Google Scholar 

  21. Lieberman IH, Togawa D, Kayanja MM, Reinhardt MK, Friedlander A, Knoller N, et al. Bone-mounted miniature robotic guidance for pedicle screw and translaminar facet screw placement: Part I—Technical development and a test case result. 2006;59(3):641–50.

    Google Scholar 

  22. Togawa D, Kayanja MM, Reinhardt MK, Shoham M, Balter A, Friedlander A, et al. Bone-mounted miniature robotic guidance for pedicle screw and translaminar facet screw placement: part 2--Evaluation of system accuracy. Neurosurgery. 2007.;60(2 Suppl 1):ONS129–39; discussion ONS39.

    Google Scholar 

  23. Delgado AF, Kits A, Bystam J, Kaijser M, Skorpil M, Sprenger T, et al. Diagnostic performance of a new multicontrast one-minute full brain exam (EPIMix) in neuroradiology: a prospective study. J Magn Reson Imaging. 2019;50(6):1824–33.

    Article  Google Scholar 

  24. Balicki M, Kyne S, Toporek G, Holthuizen R, Homan R, Popovic A, et al. Design and control of an image guided robot for spine surgery in a hybrid OR. Int J Med Robot Comput Assist Surg. 2020;

    Google Scholar 

  25. Gertzbein SD, Robbins SE. Accuracy of pedicular screw placement in vivo. Spine (Phila Pa 1976). 1990;15(1):11–4.

    Article  CAS  Google Scholar 

  26. Rampersaud YR, Simon DA, Foley KT. Accuracy requirements for image-guided spinal pedicle screw placement. Spine (Phila Pa 1976). 2001;26(4):352–9.

    Article  CAS  Google Scholar 

  27. Wanivenhaus F, Neuhaus C, Liebmann F, Roner S, Spirig JM, Farshad M. Augmented reality-assisted rod bending in spinal surgery. Spine J. 2019;19(10):1687–9.

    Article  Google Scholar 

  28. Auloge P, Cazzato RL, Ramamurthy N, de Marini P, Rousseau C, Garnon J, et al. Augmented reality and artificial intelligence-based navigation during percutaneous vertebroplasty: a pilot randomised clinical trial. Eur Spine J. 2019;

    Google Scholar 

  29. Burström G, Swamy A, Spliethoff JW, Reich C, Babic D, Hendriks BH, et al. Diffuse reflectance spectroscopy accurately identifies the pre-cortical zone to avoid impending pedicle screw breach in spinal fixation surgery. Biomed Opt Express. 2019;10(11):5905–20.

    Article  Google Scholar 

  30. Guillen PT, Knopper RG, Kroger J, Wycliffe ND, Danisa OA, Cheng WK. Independent assessment of a new pedicle probe and its ability to detect pedicle breach: a cadaveric study. J Neurosurg Spine. 2014;21(5):821–5.

    Article  Google Scholar 

  31. Fabelo H, Halicek M, Ortega S, Shahedi M, Szolna A, Piñeiro JF, et al. Deep learning-based framework for in vivo identification of glioblastoma tumor using hyperspectral images of human brain. Sensors (Basel). 2019;19(4):920.

    Article  Google Scholar 

  32. Martinez B, Leon R, Fabelo H, Ortega S, Piñeiro JF, Szolna A, et al. Most relevant spectral bands identification for brain cancer detection using hyperspectral imaging. Sensors (Basel). 2019;19(24):5481.

    Article  Google Scholar 

  33. Huang J, Halicek M, Shahedi M, Fei B, editors. Augmented reality visualization of hyperspectral imaging classifications for image-guided brain tumor resection. Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling; 2020: International Society for Optics and Photonics.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Adrian Elmi-Terander .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Elmi-Terander, A., Burström, G., Persson, O., Edström, E. (2022). Future Perspective of Augmented Reality in Minimally Invasive Spine Surgery. In: Kim, JS., Härtl, R., Wang, M.Y., Elmi-Terander, A. (eds) Technical Advances in Minimally Invasive Spine Surgery. Springer, Singapore. https://doi.org/10.1007/978-981-19-0175-1_38

Download citation

  • DOI: https://doi.org/10.1007/978-981-19-0175-1_38

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-0174-4

  • Online ISBN: 978-981-19-0175-1

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics