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Journal of Orthopaedics logoLink to Journal of Orthopaedics
. 2023 Jul 18;43:30–35. doi: 10.1016/j.jor.2023.07.018

Augmented and virtual reality in spine surgery

Andrea Luca a,∗, Riccardo Giorgino b,c
PMCID: PMC10405158  PMID: 37555206

Abstract

Augmented Reality (AR) and Virtual Reality (VR) have developed unprecedentedly in recent years, providing interesting opportunities for medical applications. Their integration into clinical assessment, surgical workflow, and training has shown tremendous potential to improve daily life activity in spine surgery. The paper explores the utilization of VR and AR in spine surgery, with their applications, benefits, challenges, and forthcoming prospects.

1. Introduction

Digital transformation represents one of the most relevant social events of the last decades, and it is credited to potentially has an epochal impact also in the healthcare landscape.

The term digital healthcare transformation refers to the integration of digital technologies into the intimate functioning process of the healthcare system: it encompasses not only the technology adoption but also the changes in the rules and mechanisms that regulate the complex and heterogeneous healthcare macrocosm.

The ultimate goal of the digital healthcare transformation is to ameliorate the operational efficiency and effectiveness of the actions that governments, institutions, and industries have to take to improve patient outcomes and healthcare access maintenance and sustainability.

Nowadays, the dissemination of technological innovations covers all the fields of modern medicine: telemedicine and remote medicine, electronic records, integrated diagnostic tools, surgical navigation systems, robots and co-bots, Virtual Reality (VR), and Augmented Reality (AR) are just a few examples of the current application of digital technologies.

Beyond the specific technical aspects that make each computer technology more efficient for specific tasks, they have to be considered all closely linked by the possibility of mutual integration of their operations: this particular aspect, the "continuity of applications," produces a further improvement of the global system far beyond the potentiality of the single technology.

Another crucial feature that must be considered to understand better the potential landscape that we will face soon is the possibility that the digital transformation process offers to instantly create accurate and reproducible metrics, generating, in a relatively limited time, a vast amount of data that can feed what is nowadays the superstars among all the technologies, the Artificial Intelligence (AI).

Undoubtedly, this futuristic landscape also carries challenges and risks that regulatory bodies should carefully face and mitigate: costs, universal access to technology for developing countries, data protection, and cybersecurity are all potential threats that should be considered and anticipated with a proactive approach.

It is possible to identify a similar scenario, on a smaller scale, also in spinal surgery. In the last decades, the massive entry of numerous technologies changed the diagnostic and therapeutic tools available for spine specialists.

Among all the available technologies, VR and AR show the greatest versatility of use and potentialities: their applications encompass remote surgery and consultation, elaboration of complex imaging data for diagnosis, treatment planning, and patient discussion, intraoperative navigation, pre and postoperative data collection.

In addition, they are particularly effective as innovative tools for medical education and surgical training.

The possibility of collecting data throughout the patient's journey, from the initial evaluation to the follow-up passing through the treatment phase (surgery or conservative treatment) allows to generate, also in spine surgery, a valuable source of information that can be effectively handled by Artificial Intelligence algorithms with multiple purposes, among which one of the most promising is represented by the elaboration of predictive models. (Fig. 1)

Fig. 1.

Fig. 1

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Scheme of assistive technologies interaction.

Several studies have been conducted on this topic: the possibility of generating predictive models based on large databases to identify specific patterns underlying the onset of adverse events after a spinal intervention could allow the transition from the "risk factor" concept to the "individualized risk" concept.1, 2, 3, 4, 5, 6, 7

The same principles apply to outcome prediction, which still is one of the most elusive targets of the current research.8,9

Most of the studies identified coding and data collection errors and data loss as common sources of bias.10

Systematic use of digital technologies, such as AR, might benefit clinicians and data analysts by reducing the risk of inappropriate data collection or loss. The aim of this paper is to explore the utilization of VR and AR in the filed of spine surgery, with their applications, benefits, challenges, and forthcoming prospects.

1.1. Augmented reality

The term “Augmented Reality” refers to a technology that allows to "augment" or expand the user's sensorial perception of the environment, overlaying digital information such as images, videos, sounds, or 3D models onto the real world.

The term was coined in the '90 by Tom Caudell, even if pioneering attempts with head-mounted displays can be traced back to the '60s and '70s.

However, it was only in the 2000s that the advancement in computer science and the release of new devices (tablets, smartphones, mini-cameras) paved the way for the mainstream adoption of this technology, allowing it to reach a broader audience.

Today, thanks to the advent of a widespread and stable connection network, AR is routinely used in many areas, including medicine.

The application of AR in medicine ranges from surgical assistance to medical education and training, rehabilitation, enhanced diagnostic visual interaction, remote assistance, and telemedicine. (Fig. 2, Fig. 3)

Fig. 2.

Fig. 2

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Experimental use of Augmented Reality Applications.

Fig. 3.

Fig. 3

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Experimental application of Augmented Reality (AR) in navigation systems for pedicle screw placement.

The success of this technology depends on the fact that hardware and software development costs are relatively low compared to other technologies, and the devices are sufficiently stable, easy to find and use. Several studies have explored the potentialities of AR also in spine surgery: Bhatt11 et al., in 2022 reported enthusiastic results of their experience with AR-assisted surgery for instrumented thoracolumbar fusion (both with open and MIS techniques): head-mounted AR devices were used to assist the pedicle screw placement in their study. The authors found an overall screw accuracy rate of 97.1% with no intraoperative or early postoperative surgical complications and no revision surgery needed the following two weeks after surgery.

Yahanda12 et al. also reported a high accuracy rate (100%) in a series of 63 percutaneous pedicle screws placed with AR guidance with a decreased radiation dose through the surgeries.

Other researchers have reported similar results.13, 14, 15

Other studies investigated the use of AR for percutaneous vertebroplasty16, anterior cervical foraminotomy, posterior cervical laminoforaminotomy17, and intraoperative osteotomy planning in corrective deformity surgery18.

The main limitations in using this technology may depend on the sensory discomfort and overload that may lead to user's disorientation, visual obstruction,13 and system malfunctions.

Harel15 reported that in his series, 2 cases out of 19 were not carried out due to technical issues with the system's intraoperative scanner.

Other limitations can be ascribed to the difficulties of assimilating the oculomotor mechanism that comes with AR: the user's actions (hand movements, physical identification of the anatomical landmarks, screw placement) follow contemporary visual stimuli from the headset display and the surrounding environment.13,19

Proper training might be required to overcome this problem which could still be limiting for susceptible subjects.

Patient selection, with the exclusion of obese patients, should be taken into account, especially during the early steps of the learning curve, because the markers used to detect anatomical landmarks might be too short.13

Another technical limitation repeatedly reported is the impossibility of using bright lights, as usually happens during the surgical procedure, to ensure the best image contrast of AR models.13,19

Despite these limitations, the application of AR technology in spine surgery allows for further implement the existing navigational systems with the advantage of reduced costs and encumbrance in the operating theatre.

Interestingly little has been reported regarding the use of this technology outside of the operating theatre in spine surgery or for different tasks rather than assisted screw placement or spinal navigation in general: this evidence can be explained by the fact that the pedicle screw positioning is still perceived, perhaps erroneously, as the most delicate moment of the surgical procedure.

Another possible explanation is that this technology is often offered as an ancillary assistive technology by the MedTech Companies that supply the implants and therefore have an interest in developing only, or almost only, the specific aspects of the technology that are functional to the use of the implants.

Undoubtedly, we are currently using only a small fraction of the real potential offered by this technology, and therefore, there can be ample room for development.

Possible future clinical application of AR includes its use for patient management and monitoring in the pre and postoperative phases, data collection, enhanced diagnostic, and patient communication.

A separate discussion must be considered for the application of AR as an educational tool: anatomy teaching is one of the most interesting applications of AR with several software being developed for this purpose.

Moro et al.20 reported on the effectiveness of using VR, AR, and other digital devices for teaching structural anatomy and found no significant differences among the three groups regarding knowledge acquisition with greater immersion and engagement in the VR and AR groups.

2. Virtual reality

VR is an interactive computer-generated experience in a simulated environment that incorporates mainly auditory, visual and other sensory feedback (e.g., haptic). This immersive environment can be similar or completely different from the real world, creating, in the latter case, an experience that is not possible in ordinary physical reality.

The immersivity of the experience aims to make users feel as if they are physically interacting with the objects and characters of the virtual world, thus increasing the user's engagement.

In a previous work, Luca et al.21 traced the first attempts at an immersive, multimodal reality simulation back to 1968 with Morton Heilig's22 milestone Sensorama, a machine that included a vibrating seat, fans, stereo-sound, and stereoscopic video systems (Fig. 4, Fig. 5).

Fig. 4.

Fig. 4

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Virtual Reality Simulation (Lateral access to the lumbar spine). Incorrect actions generate alerts (adapted pre-existing level of expertise).

Fig. 5.

Fig. 5

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Simulation of surgical procedure (lateral access to the lumbar spine via lumbotomy).

Today, VR training finds its applications in several fields, especially where demanding procedures and hazardous conditions must be safely handled to save resources and avoid unnecessary risks: considering these premises, VR finds an elective field of application in medicine and particularly in medical training, where ethical concerns heavily limit the activity of inexperienced trainees not only to highly demanding procedures but also to routine activities that might potentially have adverse effects on patients. The educational training in Medicine is pressed in between two urgent needs: first, to ensure the uppermost level of safety for the patient (“primum non nocere”) and second, to ensure the highest level of competence for the healthcare professionals through an efficient, reproducible, and measurable transfer of expertise.

Several studies support the efficacy of VR medical training in terms of skills acquisition and competence transfer in different specialties such as urology23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, gynecology35, general surgery36, otorhinolaryngology, cardiology, thoracic surgery37, neurosurgery38 and spinal surgery.21

The VR application ranges from the anatomical exploration of the human body, with different levels of complexity (guided tours, evaluation tests, interaction, and manipulation of the anatomical structures)39, 40, 41, to the simulation of routine or highly complex medical procedures, including propaedeutic training for robotic surgery.

The Author21 tested a VR simulation of the lateral lumbar access to the spine with satisfactory results: the virtual experience included simulating several pre-operative settings, and the surgical technique itself. Interestingly, the simulation has different levels of procedural complexity, which is tailored to the pre-existing level of expertise of the user (advanced beginner, competent, proficient).

During the simulation, the number and types of mistakes and their explanations are provided, generating an objective performance assessment and giving prompt feedback on the user's ability. The simulation also generates an inter-user assessment, which can help increase the user's engagement: the gamification of the procedure can motivate users into engaging in the educational activity more effectively42, 43, 44, 45.

One of the most relevant aspects of this approach is the systematic collection of the training outcomes and data, which is quite innovative: the Author recently developed a supervised machine learning algorithm, named ALFA®, to expand the number of possible actions during the simulation further by the experience of other users (specifically proficient users who completed the VR simulation).

To the Author's knowledge, this is the first attempt to associate VR medical training with artificial intelligence algorithms systematically.

Apart from its use for training purposes, VR has also been used in clinical scenarios for rehabilitation and physical therapy46,47, pain management48, 49, 50, enhanced diagnostic, and patient communication.

3. Discussion

The advent of AR and VR technologies has brought about significant transformations in various fields, and healthcare is no exception.

These technologies are widely used with different aims and purposes that finally refer to two macro-areas: medical training and clinical applications.

Lozano et al.51 claim the need for a considerable expansion of the world's health workforce, especially in low-income settings, highlighting the need for funding training programs for healthcare professionals: 43 million additional health workers are needed globally to meet universal health coverage targets.

In the field of AR applied to the medical field, it is crucial to mention devices such as smart glasses, which allow users to simultaneously see the real world and virtual images, contributing to an immersive experience in interactions and visualization.

A recent study has demonstrated the effectiveness of AR devices called head-mounted displays (HMD) in endovascular surgery, highlighting the important role of AR in image-guided surgery. This device has been successfully used to display perioperative angiography during complex procedures such as peripheral angioplasty, carotid angioplasty, and endovascular repair of an aortic aneurysm. Furthermore, to maintain the sterility of the surgical environment, a voice command activation mechanism is even provided.52 Such a device could be helpful in the accurate planning of complex cases in spinal surgery.

The field of AR in the medical domain continues to expand, with constant improvements being sought in this sector. The study by Boo et al. presented a prototype device with excellent and improved features, including resolution, field of view, overall input-output efficiency, and particularly the resolution of vergence-accommodation conflict.53

Given this scenario, the use of technologies such as VR and AR represents a viable strategy to ensure sustainable and effective training tools for large audiences of healthcare professionals, especially in developing countries or remote regions with limited access to updated medical training. The Authors, during the Spine20 meeting held in Riyadh in 2020, promoted an initiative to fill the educational gap due to socioeconomic and geographic factors: the initiative, named A.I.D., aims to create an Affordable, Interactive and Democratic, beyond any political meanings, educational platform based on the applications of digital technologies.

Regarding this specific task VR, according to the Author's belief, has several advantages compared to AR. First, the equipment and software development costs are significantly lower. Web connectivity is not a pre-requisite allowing to deliver the platform virtually everywhere.

In addition, the immersivity of the environment in VR allows to enhance the training experience with greater satisfaction for the user.

The same COVID pandemic, according to some authors, has contributed to an increase in interest in VR and AR in surgical training. Recent studies seem to conclude that the path taken appears promising, but further progress is necessary. In particular, a recent study by Jung et al. has shown that response rates are still unclear and often conducted in single-center institutions. Additional high-quality, multicenter, and long-term studies are needed to further investigate the adoption of VR/AR technologies in spinal surgery training programs.54 Greater defined technical and quality requirements seem to be necessary for devices using VR and AR, particularly intraoperative studies analyzing multiple surgical procedures in spinal surgery.55

Undoubtedly, AR has shown some promising results, as in the study by Dennler et al. that evaluated the feasibility and accuracy of AR technology to improve precision in a specific procedure in spinal surgery, such as pedicle screw insertion. This study compared the freehand technique with AR-guided technique, indicating that AR improved the precision of pedicle screw insertion in a laboratory setting, reducing the effect of surgeon experience.56

On the other hand, AR has not only been applied to basic procedures but also to more complex procedures in spinal surgery, such as interventions on spinal tumors, to precisely locate pathologies and spinal cord abnormalities. The study by Carl et al. examined the application of AR through head-up displays (HUD) of surgical microscopes in ten patients with intradural spinal tumors. This technology aided the surgeon in visualizing the tumor's surroundings and other relevant surrounding structures with close correspondence, demonstrating high precision and useful intuitive visualization of the tumor and surrounding structures.57 This successful implementation of AR technology highlights the potential for further advancements in surgical training. To enhance these training methods, potential improvements should include the implementation of deep simulations with varying levels of complexity.

A multidisciplinary approach including healthcare professionals, IT developers, and educators with specific competence in andragogy is crucial for the development of the simulations. Moreover, integrating AI algorithms into the training platforms can further enhance the effectiveness and efficiency of surgical training using AR technology.

The main limitation of the two technologies is insufficient haptic feedback, a technical issue that should be easily overcome soon with the release of multisensorial devices.

Once again, the costs to develop and use the technology represent the major limitation: Bhardwaj58 claims that uncontrolled use of expensive technology and excessive ancillary testing account for 25–30% of total healthcare costs.

On the other hand, AR needs less and less expensive equipment compared to other technologies that cover the same fields of application (optical navigation systems, robots).

Future developments, both in the hardware and software, should improve the devices' stability and handling, reducing the learning curve. According to the Author's belief, the integration with Artificial Intelligence algorithms will be the enabling factor for a massive qualitative leap of this technology.

4. Conclusion

The future of VR and AR in spine surgery appears promising.

Advancements in technology, including improved hardware, more intuitive interfaces, and sophisticated haptic feedback systems, will further enhance surgical precision and patient safety.

A fruitful collaboration among healthcare professionals, engineers, and developers is the key factor in reducing the overall costs of the technology and fulfilling real clinical and educational needs.

References

  • 1.Kimmell K.T., Algattas H., Joynt P., et al. Risk modeling predicts complication rates for spinal surgery. Spine. 2015;40(23):1836–1841. doi: 10.1097/BRS.0000000000001051. [DOI] [PubMed] [Google Scholar]
  • 2.Veeravagu A., Li A., Swinney C., et al. Predicting complication risk in spine surgery: a prospective analysis of a novel risk assessment tool. J Neurosurg Spine. 2017;27(1):81–91. doi: 10.3171/2016.12.SPINE16969. [DOI] [PubMed] [Google Scholar]
  • 3.McCarthy M.H., Singh P., Nayak R., et al. Can the American college of surgeons risk calculator predict 30-day complications after spine surgery? Spine. 2020;45(9):621–628. doi: 10.1097/BRS.0000000000003340. [DOI] [PubMed] [Google Scholar]
  • 4.Bekelis K., Desai A., Bakhoum S.F., Missios S. A predictive model of complications after spine surgery: the National Surgical Quality Improvement Program (NSQIP) 2005-2010. Spine J. 2014;14(7):1247–1255. doi: 10.1016/J.SPINEE.2013.08.009. [DOI] [PubMed] [Google Scholar]
  • 5.Charlson M.E., Pompei P., Ales K.L., MacKenzie C.R. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chron Dis. 1987;40(5):373–383. doi: 10.1016/0021-9681(87)90171-8. [DOI] [PubMed] [Google Scholar]
  • 6.Ratliff J.K., Balise R., Veeravagu A., et al. Predicting occurrence of spine surgery complications using big data modeling of an administrative claims database. J. Bone Joint Surg - American. 2016;98(10) doi: 10.2106/JBJS.15.00301. [DOI] [PubMed] [Google Scholar]
  • 7.Passias P.G., Diebo B.G., Marascalchi B.J., et al. A novel index for quantifying the risk of early complications for patients undergoing cervical spine surgeries. J Neurosurg Spine. 2017;27(5):501–507. doi: 10.3171/2017.3.SPINE16887. [DOI] [PubMed] [Google Scholar]
  • 8.Campagner A., Berjano P., Lamartina C., Langella F., Lombardi G., Cabitza F. Assessment and prediction of spine surgery invasiveness with machine learning techniques. Comput Biol Med. 2020;121 doi: 10.1016/J.COMPBIOMED.2020.103796. [DOI] [PubMed] [Google Scholar]
  • 9.Baroncini A., Migliorini F., Langella F., et al. Perioperative predictive factors for positive outcomes in spine fusion for adult deformity correction. J Clin Med. 2021;11(1) doi: 10.3390/JCM11010144. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Han S.S., Azad T.D., Suarez P.A., Ratliff J.K. A machine learning approach for predictive models of adverse events following spine surgery. Spine J. 2019;19(11):1772–1781. doi: 10.1016/J.SPINEE.2019.06.018. [DOI] [PubMed] [Google Scholar]
  • 11.Bhatt F.R., Orosz L.D., Tewari A., et al. Global Spine J; 2022. Augmented Reality-Assisted Spine Surgery: An Early Experience Demonstrating Safety and Accuracy with 218 Screws. Published online. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Yahanda A.T., Moore E., Ray W.Z., Pennicooke B., Jennings J.W., Molina C.A. First in-human report of the clinical accuracy of thoracolumbar percutaneous pedicle screw placement using augmented reality guidance. Neurosurg Focus. 2021;51(2):1–8. doi: 10.3171/2021.5.FOCUS21217. [DOI] [PubMed] [Google Scholar]
  • 13.Liu A., Jin Y., Cottrill E., et al. Clinical accuracy and initial experience with augmented reality-assisted pedicle screw placement: the first 205 screws. J Neurosurg Spine. 2021;36(3):351–357. doi: 10.3171/2021.2.SPINE202097. [DOI] [PubMed] [Google Scholar]
  • 14.Edström E., Burström G., Nachabe R., Gerdhem P., Terander A.E. 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. doi: 10.1093/ONS/OPZ236. [DOI] [PubMed] [Google Scholar]
  • 15.Harel R., Anekstein Y., Raichel M., et al. The XVS system during open spinal fixation procedures in patients requiring pedicle screw placement in the lumbosacral spine. World Neurosurg. 2022;164:e1226–e1232. doi: 10.1016/J.WNEU.2022.05.134. [DOI] [PubMed] [Google Scholar]
  • 16.Abe Y., Sato S., Kato K., et al. A novel 3D guidance system using augmented reality for percutaneous vertebroplasty: technical note. J Neurosurg Spine. 2013;19(4):492–501. doi: 10.3171/2013.7.SPINE12917. [DOI] [PubMed] [Google Scholar]
  • 17.Umebayashi D., Yamamoto Y., Nakajima Y., Fukaya N., Hara M. Augmented reality visualization-guided microscopic spine surgery: transvertebral anterior cervical foraminotomy and posterior foraminotomy. J Am Acad Orthop Surg Glob Res Rev. 2018;2(4) doi: 10.5435/JAAOSGLOBAL-D-17-00008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Kosterhon M., Gutenberg A., Kantelhardt S.R., Archavlis E., Giese A. Navigation and image injection for control of bone removal and osteotomy planes in spine surgery. Oper Neurosurg (Hagerstown). 2017;13(2):297–304. doi: 10.1093/ONS/OPW017. [DOI] [PubMed] [Google Scholar]
  • 19.Li J., Zhang H., Li Q., et al. Treating lumbar fracture using the mixed reality technique. BioMed Res Int. 2021:2021. doi: 10.1155/2021/6620746. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Moro C., Štromberga Z., Raikos A., Stirling A. The effectiveness of virtual and augmented reality in health sciences and medical anatomy. Anat Sci Educ. 2017;10(6):549–559. doi: 10.1002/ASE.1696. [DOI] [PubMed] [Google Scholar]
  • 21.Luca A., Giorgino R., Gesualdo L., et al. Innovative educational pathways in spine surgery: advanced virtual reality-based training. World Neurosurg. 2020;140:674–680. doi: 10.1016/J.WNEU.2020.04.102. [DOI] [PubMed] [Google Scholar]
  • 22.Grau O. Leonardo Books; 2004. Virtual Art: From Illusion to Immersion. [Google Scholar]
  • 23.Lendvay T.S., Casale P., Sweet R., Peters C. Initial validation of a virtual-reality robotic simulator. J Robot Surg. 2008;2(3):145–149. doi: 10.1007/s11701-008-0099-1. [DOI] [PubMed] [Google Scholar]
  • 24.Kenney P.A., Wszolek M.F., Gould J.J., Libertino J.A., Moinzadeh A. Face, content, and construct validity of dV-trainer, a novel virtual reality simulator for robotic surgery. Urology. 2009;73(6):1288–1292. doi: 10.1016/j.urology.2008.12.044. [DOI] [PubMed] [Google Scholar]
  • 25.Sethi A.S., Peine W.J., Mohammadi Y., Sundaram C.P. Validation of a novel virtual reality robotic simulator. J Endourol. 2009;23(3):503–508. doi: 10.1089/end.2008.0250. [DOI] [PubMed] [Google Scholar]
  • 26.Korets R., Mues A.C., Graversen J.A., et al. Validating the use of the Mimic dV-trainer for robotic surgery skill acquisition among urology residents. Urology. 2011 doi: 10.1016/j.urology.2011.07.1426. Published online. [DOI] [PubMed] [Google Scholar]
  • 27.Lee J.Y., Mucksavage P., Kerbl D.C., Huynh V.B., Etafy M., McDougall E.M. Validation study of a virtual reality robotic simulatorrole as an assessment tool? J Urol. 2012;187(3):998–1002. doi: 10.1016/j.juro.2011.10.160. [DOI] [PubMed] [Google Scholar]
  • 28.Liss M.A., Abdelshehid C., Quach S., et al. Validation, correlation, and comparison of the da Vinci Trainer TM and the da Vinci surgical skills simulatorTM using the MimicTM software for urologic robotic surgical education. J Endourol. 2012;26(12):1629–1634. doi: 10.1089/end.2012.0328. [DOI] [PubMed] [Google Scholar]
  • 29.Hung A.J., Shah S.H., Dalag L., Shin D., Gill I.S. Development and validation of a novel robotic procedure specific simulation platform: partial nephrectomy. J Urol. 2015;194(2):520–526. doi: 10.1016/j.juro.2015.02.2949. [DOI] [PubMed] [Google Scholar]
  • 30.Hung A.J., Patil M.B., Zehnder P., et al. Concurrent and predictive validation of a novel robotic surgery simulator: a prospective, randomized study. J Urol. 2012;187(2):630–637. doi: 10.1016/j.juro.2011.09.154. [DOI] [PubMed] [Google Scholar]
  • 31.Hung A.J., Zehnder P., Patil M.B., et al. Face, content and construct validity of a novel robotic surgery simulator. J Urol. 2011;186(3):1019–1025. doi: 10.1016/j.juro.2011.04.064. [DOI] [PubMed] [Google Scholar]
  • 32.Ramos P., Montez J., Tripp A., Ng C.K., Gill I.S., Hung A.J. Face, content, construct and concurrent validity of dry laboratory exercises for robotic training using a global assessment tool. BJU Int. 2014;113(5):836–842. doi: 10.1111/bju.12559. [DOI] [PubMed] [Google Scholar]
  • 33.Seixas-Mikelus S.A., Stegemann A.P., Kesavadas T., et al. Content validation of a novel robotic surgical simulator. BJU Int. 2011;107(7):1130–1135. doi: 10.1111/j.1464-410X.2010.09694.x. [DOI] [PubMed] [Google Scholar]
  • 34.Balasundaram I., Aggarwal R., Darzi A. Short-phase training on a virtual reality simulator improves technical performance in tele-robotic surgery. Int J Med Robot Comput Assist Surg. 2008;4(2):139–145. doi: 10.1002/rcs.181. [DOI] [PubMed] [Google Scholar]
  • 35.Lyons C., Goldfarb D., Jones S.L., et al. Which skills really matter? proving face, content, and construct validity for a commercial robotic simulator. Surg Endosc. 2013;27(6):2020–2030. doi: 10.1007/s00464-012-2704-7. [DOI] [PubMed] [Google Scholar]
  • 36.Moglia A., Ferrari V., Morelli L., et al. Distribution of innate ability for surgery amongst medical students assessed by an advanced virtual reality surgical simulator. Surg Endosc. 2014;28(6):1830–1837. doi: 10.1007/s00464-013-3393-6. [DOI] [PubMed] [Google Scholar]
  • 37.Kelly D.C., Margules A.C., Kundavaram C.R., et al. Face, content, and construct validation of the da Vinci Skills Simulator. Urology. 2012;79(5):1068–1072. doi: 10.1016/j.urology.2012.01.028. [DOI] [PubMed] [Google Scholar]
  • 38.Bernardo A. Virtual reality and simulation in neurosurgical training. World Neurosurg. 2017;106:1015–1029. doi: 10.1016/j.wneu.2017.06.140. [DOI] [PubMed] [Google Scholar]
  • 39.Izard S.G., Juanes Méndez J.A., Palomera P.R. Virtual reality educational tool for human anatomy. J Med Syst. 2017 doi: 10.1007/s10916-017-0723-6. Published online. [DOI] [PubMed] [Google Scholar]
  • 40.Stepan K., Zeiger J., Hanchuk S., et al. Immersive virtual reality as a teaching tool for neuroanatomy. Int Forum Allergy Rhinol. 2017;7(10):1006–1013. doi: 10.1002/alr.21986. [DOI] [PubMed] [Google Scholar]
  • 41.Moro C., Štromberga Z., Raikos A., Stirling A. The effectiveness of virtual and augmented reality in health sciences and medical anatomy. Anat Sci Educ. 2017 doi: 10.1002/ase.1696. Published online. [DOI] [PubMed] [Google Scholar]
  • 42.Edwards E.A., Lumsden J., Rivas C., et al. Gamification for health promotion: systematic review of behaviour change techniques in smartphone apps. BMJ Open. 2016;6(10) doi: 10.1136/bmjopen-2016-012447. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.King D., Greaves F., Exeter C., Darzi A. “Gamification”: influencing health behaviours with games. J R Soc Med. 2013;106(3):76–78. doi: 10.1177/0141076813480996. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Abdul Rahim M.I., Thomas R.H. Gamification of medication adherence in epilepsy. Seizure. 2017;52:11–14. doi: 10.1016/j.seizure.2017.09.008. [DOI] [PubMed] [Google Scholar]
  • 45.Domingos D.C.L., Lima L.F.S.G., Messias T.F., Feijo J.V.L., Diniz A.A.R., Soares H.B. 2016-October. Institute of Electrical and Electronics Engineers Inc.; 2016. Blood hero: an application for encouraging the blood donation by applying gamification; pp. 5624–5627. (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). [DOI] [PubMed] [Google Scholar]
  • 46.Tieri G., Morone G., Paolucci S., Iosa M. Virtual reality in cognitive and motor rehabilitation: facts, fiction and fallacies. Expet Rev Med Dev. 2018;15(2):107–117. doi: 10.1080/17434440.2018.1425613. [DOI] [PubMed] [Google Scholar]
  • 47.Gumaa M., Youssef A.R. Is virtual reality effective in orthopedic rehabilitation? A systematic review and meta-analysis. Phys Ther. 2019;99(10):1304–1325. doi: 10.1093/PTJ/PZZ093. [DOI] [PubMed] [Google Scholar]
  • 48.Ahmadpour N., Randall H., Choksi H., Gao A., Vaughan C., Poronnik P. Virtual Reality interventions for acute and chronic pain management. Int J Biochem Cell Biol. 2019;114 doi: 10.1016/J.BIOCEL.2019.105568. [DOI] [PubMed] [Google Scholar]
  • 49.Grassini S. Virtual reality assisted non-pharmacological treatments in chronic pain management: a systematic review and quantitative meta-analysis. Int J Environ Res Publ Health. 2022;19(7) doi: 10.3390/ijerph19074071. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Arane K., Behboudi A., Goldman R.D. Virtual reality for pain and anxiety management in children. Can Fam Physician. 2017;63(12) https://pubmed.ncbi.nlm.nih.gov/29237632/ [PMC free article] [PubMed] [Google Scholar]
  • 51.Haakenstad A., Irvine C.M.S., Knight M., et al. Measuring the availability of human resources for health and its relationship to universal health coverage for 204 countries and territories from 1990 to 2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2022;399(10341):2129–2154. doi: 10.1016/S0140-6736(22)00532-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52.Mialhe C., Chaudhuri A., Raffort J., Lareyre F. Feasibility of the application of holographic augmented reality in endovascular surgery using microsoft HoloLens head-mounted display. Ann Vasc Surg. 2021 Oct;76:597–598. doi: 10.1016/j.avsg.2021.05.010. [DOI] [PubMed] [Google Scholar]
  • 53.Boo H., Lee Y.S., Yang H., Matthews B., Lee T.G., Wong C.W. Metasurface wavefront control for high-performance user-natural augmented reality waveguide glasses. Sci Rep. 2022 Apr 6;12(1):5832. doi: 10.1038/s41598-022-09680-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 54.Jung Y., Muddaluru V., Gandhi P., Pahuta M., Guha D. The development and applications of augmented and virtual reality technology in spine surgery training: a systematic review. Can J Neurol Sci. 2023 Apr 28:1–10. doi: 10.1017/cjn.2023.46. [DOI] [PubMed] [Google Scholar]
  • 55.McCloskey K., Turlip R., Ahmad H.S., Ghenbot Y.G., Chauhan D., Yoon J.W. Virtual and augmented reality in spine surgery: a systematic review. World Neurosurg. 2023 May;173:96–107. doi: 10.1016/j.wneu.2023.02.068. [DOI] [PubMed] [Google Scholar]
  • 56.Dennler C., Jaberg L., Spirig J., et al. Augmented reality-based navigation increases precision of pedicle screw insertion. J Orthop Surg Res. 2020 May 14;15(1):174. doi: 10.1186/s13018-020-01690-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 57.Carl B., Bopp M., Saß B., Pojskic M., Nimsky C. Augmented reality in intradural spinal tumor surgery. Acta Neurochir. 2019 Oct;161(10):2181–2193. doi: 10.1007/s00701-019-04005-0. [DOI] [PubMed] [Google Scholar]
  • 58.Bhardwaj A. Promise and provisos of artificial intelligence and machine learning in healthcare. J Healthc Leader. 2022;14:113–118. doi: 10.2147/JHL.S369498. [DOI] [PMC free article] [PubMed] [Google Scholar]

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