Editorial. Navigation in spine surgery: an innovation here to stay

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  • 1 Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
Free access

The use of assistive technology for placement of pedicle screws has had a tremendous impact on the practice of spine surgery. Computer navigation and robot-assisted navigation are becoming increasingly widespread. The utility of such assistive technology has been well documented. These technologies result in improved accuracy of pedicle screw placement compared to freehand techniques, and therefore lead to fewer complications related to screw placement and less return to the operating room (OR) for malposition.1–12 With the benefit of assistive navigation clear, there has been a host of competing technologies developed and marketed to surgeons and hospital systems (Table 1). Although the nuances among the systems vary, the goal is the same: improved accuracy in placement of pedicle screws and other surgical tasks that fall under the generic heading of intraoperative navigation. The adoption of a particular platform, up to this point, has been mostly dependent on the preferences of a certain group of surgeons, a department, or a hospital system. There does not appear yet to be a clear advantage for any particular system in terms of patient outcomes or workflow. Differences exist with regard the surgical workflow, the need for an intraoperative CT scan, the process for registration, the compatibility with different surgical instruments and vendors, and other factors. Although the accuracy of all these technologies is quite excellent, the focus for further innovation seems to be improvement in the surgical workflow and the surgeon’s experience. Important work is being done in creating a registration process that is more automated, less intrusive, and less time-consuming. Gradual degradation of performance would be an improvement over the acute failure most systems now suffer from. As more and more surgical instruments are trackable, intraoperative navigation becomes ubiquitous and an expectation of the OR environment. Improvements in these areas are underway and will lead to further adoption, prevalence, and the immersion of the surgeon into a heretofore unknown operative experience.

TABLE 1.

Technologies for spinal navigation

ProductVendor
StealthStationMedtronic
Intraop spinal navigationBrainlab
Machine-vision image-guided surgery system7D Surgical
Stryker NAV3iStryker
Ziehm Vision (FD) Vario 3DZiehm
Mazor X Stealth robotMedtronic
ExcelsiusGPS robotGlobus
ROSA ONE Spine robotZimmer Biomet
TIANJI robotTinavi
xvision AR systemAugmedics
Arcadis Orbic 3DSiemens
Pulse navigationNuVasive

Liu et al. present results from placement of 205 pedicle screws in 28 patients performed using novel augmented reality (AR) assistance.13 Surgeons are fitted with an AR headset that allows for direct image projection onto the operative field and computer-navigated placement of pedicle screws. The authors found that, following independent neuroradiology review of postoperative CT scans, the screw accuracy rate was 98%, based on grade A or B Gertzbein-Robbins14 scores. The use of AR in the area of spinal instrumentation is a novel development and the authors should be commended for pioneering this new technology. The accuracy results presented, although subject to the limitations they describe well, compare favorably to other intraoperative navigation technologies currently available.15–21 Some of the potential benefits of AR-assisted technology would be the ability to simultaneously visualize the actual operative field while benefitting from computer navigation. This would help alleviate challenges associated with line-of-sight interruptions and attention shift, as the authors describe. The current version of this device does not allow for loupe magnification or a head-mounted light source, which would impede surgical workflow in certain situations but will soon be overcome. As the authors describe, screw entry points were identified based on anatomical landmarks while wearing loupes, prior to use of AR assistance for screw placement. Like other computer navigation platforms, this technology requires acquisition of an intraoperative CT scan, which may alter existing surgical workflow and increase OR time. Additionally, there remains the possibility of dislodgment of a reference array, which must be detected and addressed. Whereas the functionality of the AR technology in its current state essentially replicates the functionality of other current navigation systems, future iterations may prove valuable as the technology continues to evolve.

The future of spinal navigation will undoubtedly grow with the continued innovation of computer navigation, robotics, and AR technologies. The improved accuracy attained using these devices compared to freehand techniques is widely supported in the literature. Despite this, adoption of computer-assisted navigation has yet to become completely ubiquitous, with some estimates suggesting that only 15%–20% of eligible surgical cases use this technology. The cost-benefit analysis of these technologies has not been uniformly viewed with enthusiasm. Potential barriers to universal implementation include cost, learning curve, workflow disruption, and compatibility issues with existing vendors and implants.22

The continued expansion of this technology is likely to be dependent on a number of factors independent of pedicle screw placement accuracy. Of these, perhaps the most limiting is the startup and maintenance costs. The capital required to purchase these technologies may be restrictive for some hospital systems, as well as for independent surgical centers or in international settings. The need for personnel to run the equipment for each surgical case adds to the cost. Other needed innovations include improvement in the surgical workflow. Technologies that are cumbersome and increase overall operating time are less likely to be viewed favorably. Additionally, problems with line-of-sight interruptions and attention shift are common occurrences with most of these technologies, and strategies to avoid or minimize these disruptions to surgical workflow may increase utilization. Other considerations include the radiation exposure associated with any assistive technology. Whereas computer navigation and less reliance on intraoperative fluoroscopy have reduced radiation exposure for the surgeon, the use of intraoperative CT is likely to increase patient radiation exposure.23–26 Future innovations minimizing radiation exposure to the patient and OR staff while still achieving the needed registration would be valuable. Expanding the indications for navigation beyond pedicle screw placement may also be advantageous. For example, navigation of interbody device placement and navigated drilling are finding a receptive audience. The ability to coregister with MR images, which intracranial software does routinely, is not commonly available in spine applications and would better allow for use of navigation when dealing with soft-tissue pathologies such as discs or tumors.

The advent of 3D intraoperative navigation technology has changed the practice of spine surgery. Spinal navigation offers clear benefits to patients, with resulting surgeries that are faster and more accurate. New advancements in this domain, of which AR is one, will continue to push the field forward. The question remains which of these incremental improvements will lead to a radical transformation in the treatments that spine surgeons are able to offer their patients.

References

  • 1

    Perdomo-Pantoja A, Ishida W, Zygourakis C, Holmes C, Iyer RR, et al. Accuracy of current techniques for placement of pedicle screws in the spine: a comprehensive systematic review and meta-analysis of 51,161 screws. World Neurosurg. 2019;126:664678.e3.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 2

    Molliqaj G, Schatlo B, Alaid A, Solomiichuk V, Rohde V, et al. Accuracy of robot-guided versus freehand fluoroscopy-assisted pedicle screw insertion in thoracolumbar spinal surgery. Neurosurg Focus. 2017;42(5):E14.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 3

    Fiani B, Quadri SA, Ramakrishnan V, Berman B, Khan Y, Siddiqi J. Retrospective review on accuracy: a Pilot study of robotically guided thoracolumbar/sacral pedicle screws versus fluoroscopy-guided and computerized tomography stealth-guided screws. Cureus. 2017;9(7):e1437.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 4

    Jiang B, Karim Ahmed A, Zygourakis CC, Kalb S, Zhu AM, et al. Pedicle screw accuracy assessment in ExcelsiusGPS® robotic spine surgery: evaluation of deviation from pre-planned trajectory. Chin Neurosurg J. 2018;4:23.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 5

    Laudato PA, Pierzchala K, Schizas C. Pedicle screw insertion accuracy using O-arm, robotic guidance, or freehand technique: a comparative study. Spine (Phila Pa 1976).2018;43(6):E373E378.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 6

    Gao S, Lv Z, Fang H. Robot-assisted and conventional freehand pedicle screw placement: a systematic review and meta-analysis of randomized controlled trials. Eur Spine J. 2018;27(4):921930.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 7

    Schröder ML, Staartjes VE. Revisions for screw malposition and clinical outcomes after robot-guided lumbar fusion for spondylolisthesis. Neurosurg Focus. 2017;42(5):E12.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 8

    Liu H, Chen W, Wang Z, Lin J, Meng B, Yang H. Comparison of the accuracy between robot-assisted and conventional freehand pedicle screw placement: a systematic review and meta-analysis. Int J CARS. 2016;11(12):22732281.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 9

    Keric N, Doenitz C, Haj A, Rachwal-Czyzewicz I, Renovanz M, et al. Evaluation of robot-guided minimally invasive implantation of 2067 pedicle screws. Neurosurg Focus. 2017;42(5):E11.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 10

    Mason A, Paulsen R, Babuska JM, Rajpal S, Burneikiene S, et al. The accuracy of pedicle screw placement using intraoperative image guidance systems. J Neurosurg Spine. 2014;20(2):196203.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 11

    Parker SL, McGirt MJ, Farber SH, Amin AG, Rick AM, et al. Accuracy of free-hand pedicle screws in the thoracic and lumbar spine: analysis of 6816 consecutive screws. Neurosurgery. 2011;68(1):170178.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 12

    Chan A, Parent E, Narvacan K, San C, Lou E. Intraoperative image guidance compared with free-hand methods in adolescent idiopathic scoliosis posterior spinal surgery: a systematic review on screw-related complications and breach rates. Spine J. 2017;17(9):12151229.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 13

    Liu A, Jin Y, Cottrill E, Khan M, Westbroek E, et al. Clinical accuracy and initial experience with augmented reality–assisted pedicle screw placement: the first 205 screws. J Neurosurg Spine. Published online October 8, 2021. doi:10.3171/2021.2.SPINE202097

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 14

    Gertzbein SD, Robbins SE. Accuracy of pedicular screw placement in vivo. Spine (Phila Pa 1976).1990;15(1):1114.

  • 15

    van Dijk JD, van den Ende RP, Stramigioli S, Köchling M, Höss N. Clinical pedicle screw accuracy and deviation from planning in robot-guided spine surgery: robot-guided pedicle screw accuracy. Spine (Phila Pa 1976).2015;40(17):E986E991.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 16

    Onen MR, Simsek M, Naderi S. Robotic spine surgery: a preliminary report. Turk Neurosurg. 2014;24(4):512518.

  • 17

    Khan A, Meyers JE, Siasios I, Pollina J. Next-generation robotic spine surgery: first report on feasibility, safety, and learning curve. Oper Neurosurg (Hagerstown). 2019;17(1):6169.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 18

    Huntsman KT, Ahrendtsen LA, Riggleman JR, Ledonio CG. Robotic-assisted navigated minimally invasive pedicle screw placement in the first 100 cases at a single institution. J Robot Surg. 2020;14(1):199203.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 19

    Jiang B, Pennington Z, Zhu A, Matsoukas S, Ahmed AK, et al. Three-dimensional assessment of robot-assisted pedicle screw placement accuracy and instrumentation reliability based on a preplanned trajectory. J Neurosurg Spine. 2020;33(4):519528.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 20

    Lonjon N, Chan-Seng E, Costalat V, Bonnafoux B, Vassal M, Boetto J. Robot-assisted spine surgery: feasibility study through a prospective case-matched analysis. Eur Spine J. 2016;25(3):947955.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 21

    Han X, Tian W, Liu Y, Liu B, He D, et al. Safety and accuracy of robot-assisted versus fluoroscopy-assisted pedicle screw insertion in thoracolumbar spinal surgery: a prospective randomized controlled trial. J Neurosurg Spine. 2019;30(5):615622.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 22

    Vaccaro AR, Panchmatia JR, Kaye D, Prasad SK. Barriers to adoption of new technology. In: Navigation and Robotics in Spine Surgery.1st ed. Thieme Medical Publishers;2019.

    • Search Google Scholar
    • Export Citation
  • 23

    Mendelsohn D, Strelzow J, Dea N, Ford NL, Batke J, et al. Patient and surgeon radiation exposure during spinal instrumentation using intraoperative computed tomography-based navigation. Spine J. 2016;16(3):343354.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 24

    Bandela JR, Jacob RP, Arreola M, Griglock TM, Bova F, Yang M. Use of CT-based intraoperative spinal navigation: management of radiation exposure to operator, staff, and patients. World Neurosurg. 2013;79(2):390394.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 25

    Nottmeier EW, Bowman C, Nelson KL. Surgeon radiation exposure in cone beam computed tomography-based, image-guided spinal surgery. Int J Med Robot. 2012;8(2):196200.

  • 26

    Lange J, Karellas A, Street J, Eck JC, Lapinsky A, et al. Estimating the effective radiation dose imparted to patients by intraoperative cone-beam computed tomography in thoracolumbar spinal surgery. Spine (Phila Pa 1976).2013;38(5):E306E312.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
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  • 1 Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland

Response

Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland

Given our ever-changing, technology-driven world, it is difficult to remember a time without certain technologies. In cranial neurosurgery, real-time navigation/image guidance with such tools as Brainlab is used today without second thought. However, this was not always the case. In an early description of Brainlab’s VectorVision System, Gumprecht et al. described the system’s accuracy as well as limitations: additional OR time, marker registration, system failures/computer malfunctions, line-of-sight interruption, and the inherent learning curve1—issues still relevant to any new navigation system, including AR head-mounted display (HMD) technology, for which we describe our initial experience.

Any new innovation or technology must overcome a healthy amount of scrutiny and skepticism before being accepted and adopted. Diffusion of innovation theory, or the technology adoption lifecycle, is a social science theory developed by E. M. Rogers to describe “the process in which an innovation is communicated through certain channels over time among the members of a social system.”2 Rogers describes five adopter categories: innovators, early adopters, early majority, late majority, and laggards. This model was adapted by G. A. Moore to categorize innovators and early adopters into an early market base as compared to the mainstream market (pragmatists, conservatives, and skeptics) (Fig. 1).3 Moore also describes the “chasm” as a gap between the early market and the mainstream market, and new technologies must successfully “cross the chasm” in order to achieve mass adoption.

FIG. 1
FIG. 1

Moore’s technology adoption lifecycle graph3 highlighting the chasm between the early market and the mainstream market. Reproduced from Kindredgrey: Technology adoption life cycle—breaking the chasm. Creative Commons. Published July 2, 2020. Accessed April 27, 2021. https://search.creativecommons.org/photos/e26e60c5-a2f9-4e03-9f4c-0783ee843b74. CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0).

Currently, AR-HMD technology remains in its infancy, used by innovators and early adopters of the early market. At the time of our manuscript acceptance, AR-HMD was used at 17 hospitals, with 200+ cases completed. In just 3 months, the technology is now used at 25 hospitals, with 450+ cases completed. We anticipate these numbers will continue to increase; however, to cross the chasm into the mainstream market, several issues (as delineated by Driver and Groff in their editorial) must be addressed. These include cost, learning curve, workflow disruption, compatibility issues with existing vendors/implants, radiation exposure, co-registration with MRI, and expansion of indications.

Currently, the Augmedics xvision system (including two headsets, instrument sets, and the computer) costs approximately $150,000, with disposables costing approximately $1500 per case.4 The system is vendor “agnostic,” allowing use with any spinal implant company’s products. Issues with workflow disruption often go hand in hand with the learning curve, and improvements in these areas are already being conceptualized and developed. As additional surgeons begin to adopt the technology, more applications, indications, and creative uses of AR-HMD will emerge. Already in development and soon to hit the market pending additional FDA approval are the following improvements: compatibility with loupe magnification; a better head-mounted light source; a much lighter headset (essentially glasses); registration with preoperative CT scans; and improved reference array design. Additional developments involve the expansion of applications to include navigated interbody placement, drilling, and osteotomies; cervical spine and perhaps cranial application; and scanning technology to integrate AR-guided navigation with preoperative MRI. As with Brainlab for cranial applications, we suspect that in 20 years, AR-HMD will be a technology that is routinely used in the OR without a second thought.

References

  • 1

    Gumprecht HK, Widenka DC, Lumenta CB. BrainLab VectorVision Neuronavigation System: technology and clinical experiences in 131 cases. Neurosurgery. 1999;44(1):97105.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 2

    Rogers EM. Diffusion of Innovations. Free Press of Glencoe;1962.

  • 3

    Moore GA. Crossing the Chasm: Marketing and Selling High-Tech Products to Mainstream Customers. HarperBusiness;1991.

  • 4

    Dibble CF, Molina CA. Device profile of the XVision-spine (XVS) augmented-reality surgical navigation system: overview of its safety and efficacy. Expert Rev Med Devices. 2021;18(1):18.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation

Illustration from Dibble et al. (pp 498–508). Copyright Neurosurgery, Washington University School of Medicine. Published with permission.

  • 1

    Perdomo-Pantoja A, Ishida W, Zygourakis C, Holmes C, Iyer RR, et al. Accuracy of current techniques for placement of pedicle screws in the spine: a comprehensive systematic review and meta-analysis of 51,161 screws. World Neurosurg. 2019;126:664678.e3.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 2

    Molliqaj G, Schatlo B, Alaid A, Solomiichuk V, Rohde V, et al. Accuracy of robot-guided versus freehand fluoroscopy-assisted pedicle screw insertion in thoracolumbar spinal surgery. Neurosurg Focus. 2017;42(5):E14.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 3

    Fiani B, Quadri SA, Ramakrishnan V, Berman B, Khan Y, Siddiqi J. Retrospective review on accuracy: a Pilot study of robotically guided thoracolumbar/sacral pedicle screws versus fluoroscopy-guided and computerized tomography stealth-guided screws. Cureus. 2017;9(7):e1437.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 4

    Jiang B, Karim Ahmed A, Zygourakis CC, Kalb S, Zhu AM, et al. Pedicle screw accuracy assessment in ExcelsiusGPS® robotic spine surgery: evaluation of deviation from pre-planned trajectory. Chin Neurosurg J. 2018;4:23.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 5

    Laudato PA, Pierzchala K, Schizas C. Pedicle screw insertion accuracy using O-arm, robotic guidance, or freehand technique: a comparative study. Spine (Phila Pa 1976).2018;43(6):E373E378.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 6

    Gao S, Lv Z, Fang H. Robot-assisted and conventional freehand pedicle screw placement: a systematic review and meta-analysis of randomized controlled trials. Eur Spine J. 2018;27(4):921930.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 7

    Schröder ML, Staartjes VE. Revisions for screw malposition and clinical outcomes after robot-guided lumbar fusion for spondylolisthesis. Neurosurg Focus. 2017;42(5):E12.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 8

    Liu H, Chen W, Wang Z, Lin J, Meng B, Yang H. Comparison of the accuracy between robot-assisted and conventional freehand pedicle screw placement: a systematic review and meta-analysis. Int J CARS. 2016;11(12):22732281.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 9

    Keric N, Doenitz C, Haj A, Rachwal-Czyzewicz I, Renovanz M, et al. Evaluation of robot-guided minimally invasive implantation of 2067 pedicle screws. Neurosurg Focus. 2017;42(5):E11.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 10

    Mason A, Paulsen R, Babuska JM, Rajpal S, Burneikiene S, et al. The accuracy of pedicle screw placement using intraoperative image guidance systems. J Neurosurg Spine. 2014;20(2):196203.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 11

    Parker SL, McGirt MJ, Farber SH, Amin AG, Rick AM, et al. Accuracy of free-hand pedicle screws in the thoracic and lumbar spine: analysis of 6816 consecutive screws. Neurosurgery. 2011;68(1):170178.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 12

    Chan A, Parent E, Narvacan K, San C, Lou E. Intraoperative image guidance compared with free-hand methods in adolescent idiopathic scoliosis posterior spinal surgery: a systematic review on screw-related complications and breach rates. Spine J. 2017;17(9):12151229.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 13

    Liu A, Jin Y, Cottrill E, Khan M, Westbroek E, et al. Clinical accuracy and initial experience with augmented reality–assisted pedicle screw placement: the first 205 screws. J Neurosurg Spine. Published online October 8, 2021. doi:10.3171/2021.2.SPINE202097

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 14

    Gertzbein SD, Robbins SE. Accuracy of pedicular screw placement in vivo. Spine (Phila Pa 1976).1990;15(1):1114.

  • 15

    van Dijk JD, van den Ende RP, Stramigioli S, Köchling M, Höss N. Clinical pedicle screw accuracy and deviation from planning in robot-guided spine surgery: robot-guided pedicle screw accuracy. Spine (Phila Pa 1976).2015;40(17):E986E991.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 16

    Onen MR, Simsek M, Naderi S. Robotic spine surgery: a preliminary report. Turk Neurosurg. 2014;24(4):512518.

  • 17

    Khan A, Meyers JE, Siasios I, Pollina J. Next-generation robotic spine surgery: first report on feasibility, safety, and learning curve. Oper Neurosurg (Hagerstown). 2019;17(1):6169.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 18

    Huntsman KT, Ahrendtsen LA, Riggleman JR, Ledonio CG. Robotic-assisted navigated minimally invasive pedicle screw placement in the first 100 cases at a single institution. J Robot Surg. 2020;14(1):199203.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 19

    Jiang B, Pennington Z, Zhu A, Matsoukas S, Ahmed AK, et al. Three-dimensional assessment of robot-assisted pedicle screw placement accuracy and instrumentation reliability based on a preplanned trajectory. J Neurosurg Spine. 2020;33(4):519528.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 20

    Lonjon N, Chan-Seng E, Costalat V, Bonnafoux B, Vassal M, Boetto J. Robot-assisted spine surgery: feasibility study through a prospective case-matched analysis. Eur Spine J. 2016;25(3):947955.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 21

    Han X, Tian W, Liu Y, Liu B, He D, et al. Safety and accuracy of robot-assisted versus fluoroscopy-assisted pedicle screw insertion in thoracolumbar spinal surgery: a prospective randomized controlled trial. J Neurosurg Spine. 2019;30(5):615622.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 22

    Vaccaro AR, Panchmatia JR, Kaye D, Prasad SK. Barriers to adoption of new technology. In: Navigation and Robotics in Spine Surgery.1st ed. Thieme Medical Publishers;2019.

    • Search Google Scholar
    • Export Citation
  • 23

    Mendelsohn D, Strelzow J, Dea N, Ford NL, Batke J, et al. Patient and surgeon radiation exposure during spinal instrumentation using intraoperative computed tomography-based navigation. Spine J. 2016;16(3):343354.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 24

    Bandela JR, Jacob RP, Arreola M, Griglock TM, Bova F, Yang M. Use of CT-based intraoperative spinal navigation: management of radiation exposure to operator, staff, and patients. World Neurosurg. 2013;79(2):390394.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 25

    Nottmeier EW, Bowman C, Nelson KL. Surgeon radiation exposure in cone beam computed tomography-based, image-guided spinal surgery. Int J Med Robot. 2012;8(2):196200.

  • 26

    Lange J, Karellas A, Street J, Eck JC, Lapinsky A, et al. Estimating the effective radiation dose imparted to patients by intraoperative cone-beam computed tomography in thoracolumbar spinal surgery. Spine (Phila Pa 1976).2013;38(5):E306E312.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 1

    Gumprecht HK, Widenka DC, Lumenta CB. BrainLab VectorVision Neuronavigation System: technology and clinical experiences in 131 cases. Neurosurgery. 1999;44(1):97105.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 2

    Rogers EM. Diffusion of Innovations. Free Press of Glencoe;1962.

  • 3

    Moore GA. Crossing the Chasm: Marketing and Selling High-Tech Products to Mainstream Customers. HarperBusiness;1991.

  • 4

    Dibble CF, Molina CA. Device profile of the XVision-spine (XVS) augmented-reality surgical navigation system: overview of its safety and efficacy. Expert Rev Med Devices. 2021;18(1):18.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation

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