Robot-assisted and augmented reality–assisted spinal instrumentation: a systematic review and meta-analysis of screw accuracy and outcomes over the last decade

Matthew A. Tovar School of Medicine and Health Sciences, George Washington University, Washington, DC;

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Ehsan Dowlati Department of Neurosurgery, MedStar Georgetown University Hospital, Washington, DC;

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David Y. Zhao Department of Neurosurgery, MedStar Georgetown University Hospital, Washington, DC;

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Ziam Khan Center for Bioinformatics and Computational Biology, University of Maryland, Baltimore County, Baltimore, Maryland; and

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Kory B. D. Pasko Georgetown University School of Medicine, Washington, DC

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Faheem A. Sandhu Department of Neurosurgery, MedStar Georgetown University Hospital, Washington, DC;

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Jean-Marc Voyadzis Department of Neurosurgery, MedStar Georgetown University Hospital, Washington, DC;

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OBJECTIVE

The use of technology-enhanced methods in spine surgery has increased immensely over the past decade. Here, the authors present the largest systematic review and meta-analysis to date that specifically addresses patient-centered outcomes, including the risk of inaccurate screw placement and perioperative outcomes in spinal surgeries using robotic instrumentation and/or augmented reality surgical navigation (ARSN).

METHODS

A systematic review of the literature in the PubMed, EMBASE, Web of Science, and Cochrane Library databases spanning the last decade (January 2011–November 2021) was performed to present all clinical studies comparing robot-assisted instrumentation and ARSN with conventional instrumentation techniques in lumbar spine surgery. The authors compared these two technologies as they relate to screw accuracy, estimated blood loss (EBL), intraoperative time, length of stay (LOS), perioperative complications, radiation dose and time, and the rate of reoperation.

RESULTS

A total of 64 studies were analyzed that included 11,113 patients receiving 20,547 screws. Robot-assisted instrumentation was associated with less risk of inaccurate screw placement (p < 0.0001) regardless of control arm approach (freehand, fluoroscopy guided, or navigation guided), fewer reoperations (p < 0.0001), fewer perioperative complications (p < 0.0001), lower EBL (p = 0.0005), decreased LOS (p < 0.0001), and increased intraoperative time (p = 0.0003). ARSN was associated with decreased radiation exposure compared with robotic instrumentation (p = 0.0091) and fluoroscopy-guided (p < 0.0001) techniques.

CONCLUSIONS

Altogether, the pooled data suggest that technology-enhanced thoracolumbar instrumentation is advantageous for both patients and surgeons. As the technology progresses and indications expand, it remains essential to continue investigations of both robotic instrumentation and ARSN to validate meaningful benefit over conventional instrumentation techniques in spine surgery.

ABBREVIATIONS

AR = augmented reality; ARSN = AR surgical navigation; EBL = estimated blood loss; IOT = intraoperative time; LOS = length of stay; RCT = randomized controlled trial; RR = risk ratio.

OBJECTIVE

The use of technology-enhanced methods in spine surgery has increased immensely over the past decade. Here, the authors present the largest systematic review and meta-analysis to date that specifically addresses patient-centered outcomes, including the risk of inaccurate screw placement and perioperative outcomes in spinal surgeries using robotic instrumentation and/or augmented reality surgical navigation (ARSN).

METHODS

A systematic review of the literature in the PubMed, EMBASE, Web of Science, and Cochrane Library databases spanning the last decade (January 2011–November 2021) was performed to present all clinical studies comparing robot-assisted instrumentation and ARSN with conventional instrumentation techniques in lumbar spine surgery. The authors compared these two technologies as they relate to screw accuracy, estimated blood loss (EBL), intraoperative time, length of stay (LOS), perioperative complications, radiation dose and time, and the rate of reoperation.

RESULTS

A total of 64 studies were analyzed that included 11,113 patients receiving 20,547 screws. Robot-assisted instrumentation was associated with less risk of inaccurate screw placement (p < 0.0001) regardless of control arm approach (freehand, fluoroscopy guided, or navigation guided), fewer reoperations (p < 0.0001), fewer perioperative complications (p < 0.0001), lower EBL (p = 0.0005), decreased LOS (p < 0.0001), and increased intraoperative time (p = 0.0003). ARSN was associated with decreased radiation exposure compared with robotic instrumentation (p = 0.0091) and fluoroscopy-guided (p < 0.0001) techniques.

CONCLUSIONS

Altogether, the pooled data suggest that technology-enhanced thoracolumbar instrumentation is advantageous for both patients and surgeons. As the technology progresses and indications expand, it remains essential to continue investigations of both robotic instrumentation and ARSN to validate meaningful benefit over conventional instrumentation techniques in spine surgery.

In Brief

Use of technology-enhanced thoracolumbar instrumentation with robotic and augmented reality assistance continues to grow in the field of spinal surgery. The authors aimed to quantify the magnitude of benefit imparted by these technologies with respect to screw accuracy and other patient-centered perioperative outcomes. Technology-enhanced thoracolumbar instrumentation is advantageous for both patients and surgeons. This observed patient benefit is paramount as more institutions choose to invest fiscal resources in these advanced technologies.

Transpedicular screw placement in the lumbar spine is the workhorse of posterior instrumented stabilization techniques to achieve solid three-column fixation. Misaligned screw placement has the potential to increase hospital length of stay (LOS), cause acute and chronic pain to the patient, give rise to deep infection, and induce neurovascular injury resulting in loss of sensation, paresthesia, weakness, and paralysis with increased hospital LOS.1

The fields of robot-assisted surgery and augmented reality surgical navigation (ARSN) have sought to reduce adverse outcomes in transpedicular screw placement by enhancing the accuracy of the procedure. Compared to conventional instrumentation techniques, prior studies have shown that robot-assisted surgery, defined as machines that physically guide the surgeon’s hand through the operation or remote-controlled machines that directly manipulate instruments, has comparable or superior patient outcomes and screw accuracy according to the Gertzbein-Robbins classification, less exposure to radiation, and decreased LOS.2 Similarly, augmented reality (AR), which provides a transparent 3D vantage point of the surgical anatomy and assists in planning the operation, also results in enhanced patient outcomes.3 Because of the rising popularity of this method, studies describing the utility, clinical accuracy, and efficacy of robot-assisted instrumentation and ARSN have similarly increased over the last decade (Fig. 1A).

FIG. 1.
FIG. 1.

A: Bar graph of PubMed search results of studies involving robotic instrumentation (gray) and ARSN (yellow) for spinal surgery over the last decade. B: PRISMA flow diagram of the systematic literature search for this systematic review and meta-analysis. Data added to the PRISMA template [from Moher D, Liberati A, Tetzlaff J, Altman DG, The PRISMA Group (2009). Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. PLoS Med. 6(7):e1000097] under the terms of the Creative Commons Attribution License. Figure is available in color online only.

Without these innovations, surgeons traditionally navigate the patient’s anatomy using fluoroscopy or anatomical landmarks alone to perform instrumentation in spine surgery. Generous use of fluoroscopy exposes the patient and operating room staff to significant amounts of radiation4 and requires intraoperative and postoperative revisions for up to 4.0% of screws.5 Each revision has been projected to cost the patient or the institution an average of $41,631.6 Thus, it can be seen how hospitals and physicians and the patients they serve will all benefit from increasing screw placement accuracy and decreasing the rate of complications.

This study presents a systematic review and meta-analysis of technology-enhanced spinal instrumentation, directly comparing both screw accuracy and various measures of patient-centered perioperative outcomes among robot-assisted, ARSN, and "conventional" (comprising fluoroscopy-guided and/or freehand techniques) spinal screw instrumentation. Patient-centered outcomes explored in this study included estimated blood loss (EBL), radiation dose and time, intraoperative time (IOT), LOS, incidence of perioperative complications, and rate of revisions. To date, this is the largest meta-analysis that directly compares the accuracy and patient-centered perioperative outcomes in spinal instrumentation. Additionally, this is the first study to examine the current state of AR navigation techniques as a potential competitor to more established approaches.

Methods

This review was registered with PROSPERO under registration no. 283631. The protocol is available on request, and data collection forms, data extracted from included studies, data used for all analyses, analytical code, and any other materials used in the review are available upon request.

Search Strategy

A systematic review and meta-analysis were performed according to the recommendations of the Cochrane Handbook for Systematic Reviews of Interventions. Institutional review board approval was not necessary for this study. The review followed PRISMA guidelines. Relevant literature searches were performed via PubMed, EMBASE, Web of Science, and the Cochrane Library for publication dates over the last decade (between January 1, 2011, and November 20, 2021). Keywords used to identify relevant studies were "spinal OR spine," "robot OR robotics," "augmented reality OR virtual reality."

Inclusion and Exclusion Criteria

Our search was limited to original research studies available in the English language. Two independent authors (Z.K. and D.Y.Z.) screened each search result to determine each study’s qualification for our inclusion criteria. Any disagreement was resolved through discussion and decision by a third author (E.D.). Inclusion criteria for robot-guided studies included randomized controlled trials (RCTs) and retrospective and prospective case series with more than 4 patients involving adult human subjects undergoing posterior lumbar or thoracolumbar surgery. Exclusion criteria included academic conference papers; meeting abstracts; case reports; studies investigating anterior, lateral, or cervical spine approaches; lack of a comparative control cohort; and cadaver or animal model studies. For ARSN studies, inclusion criteria included all studies using ARSN modality in patients undergoing spinal surgery for any indication. Additionally, single-cohort studies for ARSN were included.

Data Extraction

Each article was reviewed by two independent authors (Z.K. and D.Y.Z.) and the following data were recorded: study design, participant characteristics, screw accuracy, EBL, IOT, LOS, perioperative surgical complications, duration of radiation exposure, and rate of revisions. With specific regard to screw accuracy, most studies used a Gertzbein-Robbins classification, where a grade A (fully intrapedicular screw without pedicle breach) or grade B (screw that exceeds the pedicle cortex by < 2 mm) placement was deemed accurate, and grade C, D, and E (screw exceeding the pedicle by 2–4 mm, 4–6 mm, and > 6 mm, respectively) placement was deemed inaccurate. Surgical revision was defined per study but generally included return to the operating room within 180 days for instrumentation-related factors related to the initial surgery, namely, significant screw malposition or pseudarthrosis.

Risk-of-Bias Assessment

Two authors (K.B.D.P. and D.Y.Z.) independently evaluated each study. Any disagreement was resolved through discussion and decision by a third author (M.A.T.). Risk of bias was assessed using the Newcastle-Ottawa Scale for any nonrandomized studies included in the meta-analysis. Each study was evaluated based on 3 quality parameters: selection, comparability, and outcome, divided in total across 8 specific questions. For RCTs included in this study, risk of bias was assessed according to the Cochrane Collaboration risk-of-bias tool. Risk of bias was assessed based on a total of 7 items: random sequence generation (selection bias), allocation concealment (selection bias), blinding of participants and personnel (performance bias), blinding of outcome assessment (detection bias), incomplete outcome data (attrition bias), selective reporting (reporting bias), and other bias. Scoring was determined using the questions and algorithm provided by the Cochrane Collaboration, and each category was scored as unclear, low, some concern, or high risk of bias.

Data Analysis

Data were analyzed using Cochrane’s Review Manager (RevMan version 5.4). Data were input into the RevMan software, and the risk ratio for dichotomous variables was calculated using the Cochran-Mantel-Haenszel method and represented using a random-effects model. For continuous variables, the mean difference was calculated using an inverse-variance method and represented using a fixed- or random-effects model depending on the I2 index. Forest and funnel plots were generated and exported from RevMan version 5.4. Continuous variables associated with ARSN were analyzed using GraphPad Prism version 8. Statistical significance for continuous data was measured using the Student t-test with Welch’s correction for unequal variance, with statistical significance set to α = 0.05.

Results

Study Characteristics

A total of 3845 articles were extracted from the literature search as detailed in Methods; 2608 articles were removed because they were duplicates. Thus, 1237 articles were screened via review of each study’s title and abstract, and 1083 articles failed to meet primary screening. A total of 154 articles underwent full-text screening, and 90 were subsequently excluded because they were not human studies, lacked a control group, were case reports, or did not involve thoracolumbar screw instrumentation (Fig. 1B). Of the 64 remaining studies, 48 involved robot-assisted navigation (Table 1), and 16 studies involved ARSN systems (Table 2). Altogether, the included studies comprised 11,113 patients (5347 patients with robotic assistance, 293 patients with AR assistance, and 5473 with conventional instrumentation). Of studies that reported instrumentation numbers, there were 20,547 total screws instrumented (8045 robot-assisted screws, 1656 AR-assisted screws, and 12,291 conventionally placed screws).

TABLE 1.

Summary of the studies included in this systematic review involving robot-assisted spinal instrumentation

Authors & YearCountryStudy PeriodStudy DesignControl GroupRobot TypeControl, nRobot, nOutcome Measures
Kantelhardt et al., 201143Germany2006–2009Retrospective cohortOpen/MIS fluoroscopySpineAssist5755 Gertzbein-Robbins grade; radiation exposure; complications; op time; revision rate
Schizas et al., 201234SwitzerlandNRProspective cohortOpen freehandSpineAssist2311 Rampersaud classification; radiation exposure
Ringel et al., 201236GermanyNRRCTOpen fluoroscopySpineAssist3030 Gertzbein-Robbins grade; radiation time; op time; revision rates; learning curve
Roser et al., 201335Germany2010RCTOpen fluoroscopySpineAssist1918 Gertzbein-Robbins grade; radiation exposure; op time
Schatlo et al., 201437Switzerland2007–2011Retrospective cohortOpen fluoroscopySpineAssist4055 Gertzbein-Robbins grade; complications; op time; opioid use
Kim et al., 20158S. Korea2013–2014RCTOpen freehandRenaissance2020Gertzbein-Robbins grade; op time; cumulative summation test; revision rate
Lonjon et al., 201644France2013Prospective cohortOpen freehandROSA 1010Gertzbein-Robbins grade; Youkilis classification; radiation exposure; op time
Kim et al., 20179S. Korea2013–2014RCTOpen freehandRenaissance4137Gertzbein-Robbins grade; radiation exposure; time to ambulation; learning curve; postop drainage; op time
Keric et al., 201712Germany2009–2014Retrospective cohortOpen fluoroscopyRenaissance6624Weisner classification; complications; revision rate; op time; radiation time; ODI score; VAS score
Fan et al., 201738China2013–2017Retrospective cohortOpen fluoroscopy; open navigation; open templateSpineAssist15139Gertzbein-Robbins grade; angular deviation; LOS; blood loss; complications; revision rate; radiation time
Molliqaj et al., 201739Switzerland2007–2015Retrospective cohortOpen fluoroscopySpineAssist7198Gertzbein-Robbins grade
Solomiichuk et al., 201740Switzerland2009–2015Retrospective cohortOpen fluoroscopySpineAssist3535Gertzbein-Robbins grade; complications; radiation time; direction of misplacement; op time
Hyun et al., 20177S. Korea2013–2015RCTOpen fluoroscopyRenaissance3030Gertzbein-Robbins grade; radiation exposure; ODI score; VAS score; proximal facet violations; LOS; complications; revision rate; learning curve
Kim et al., 201711S. KoreaNRRCTOpen freehandRenaissance55Intradiscal pressure; facet contact forces; revision rates; proximal facet joint violation
Tian et al., 201725ChinaNRRCTOpen fluoroscopyTiRobot1723Gertzbein-Robbins grade; op time; deviation from planned trajectory
Kim et al., 201810S. Korea2013–2015RCTOpen freehandRenaissance4137ODI score; VAS score; fusion status; SF-36 score; adjacent-segment degeneration
Alaid et al., 201841Germany2007–2016Retrospective cohortOpen fluoroscopySpineAssist10898Wound revision; radiation exposure; construct failure; duration of antibiotic treatment
Fan et al., 201815China2009–2016Retrospective cohortOpen navigation; open templateRenaissance18483Gertzbein-Robbins grade; scoliosis parameters; op time; radiation exposure; LOS; complications; revision rate
Laudato et al., 201842SwitzerlandNRRetrospective cohortOpen fluoroscopy; open navigationSpineAssist7311Gertzbein-Robbins grade; neurological injury; revision rate
Le et al., 201829China2015–2018Retrospective cohortOpen fluoroscopyTiRobot3820Gertzbein-Robbins grade; op time; radiation exposure; surgical site infection; revision rate
Park et al., 201813S. Korea2013–2014RCTOpen freehandRenaissance2530Adjacent-segment degeneration; ODI score; VAS score; revision rate
Shillingford et al., 201816US2015–2016Retrospective cohortOpen freehandRenaissance2823Cortical breach; breach direction; screw angle
Archavlis et al., 201817Germany2015–2016Retrospective cohortOpen/MIS fluoroscopyRenaissance13658Facet joint violation
Faissal et al., 201814US2011–2013Retrospective cohortMIS fluoroscopyRenaissance9999Gertzbein-Robbins grade; ODI; revision rate
Han et al., 201926China2016–2017RCTOpen fluoroscopyTiRobot119115Gertzbein-Robbins grade; op time; blood loss; conversion to freehand; LOS; revision rate; radiation exposure
Yang et al., 201918China2017Retrospective cohortMIS fluoroscopyRenaissance3030Neo grade; "severe" outcome
Zhang et al., 201923China2016–2018Prospective non-RCTOpen fluoroscopyTiRobot5050Gertzbein-Robbins grade; op time; LOS; revision rate; radiation exposure
Lieber et al., 201953US2010–2014Retrospective cohortNRVarious257257Complications; LOS
Khan et al., 201946US2017Retrospective cohortMIS navigationMazor X Stealth4950Ravi grade; radiation dose; op time; LOS; blood loss
Feng et al., 201928China2016–2018RCTOpen fluoroscopyTiRobot4040Gertzbein-Robbins grade; op time; blood loss; LOS
Zhang et al., 201924China2016–2017Prospective non-RCTOpen fluoroscopyTiRobot4443Gertzbein-Robbins grade; complications; radiation time; revision rate; LOS; op time; blood loss
Zhang et al., 202120China2018–2019Retrospective cohortOpen freehandRenaissance4239Gertzbein-Robbins grade; op time; Babu’s classification; radiation time; blood loss; complications
Ver et al., 202047USNRRetrospective cohortOpen freehand; open navigationMazor X Stealth10452LOS; op time; blood loss; hospital cost
Chen et al., 202030China2018–2019Retrospective cohortOpen freehandTiRobot6631Gertzbein-Robbins grade; op time; blood loss; LOS; revision rate; SRS-22 score; scoliosis parameters
Li et al., 201950China2018–2018RCTOpen fluoroscopyOrthobot 107Gertzbein-Robbins grade; op time; LOS; blood loss; radiation time; complications
Du et al., 202031China2017–2019Retrospective cohortOpen/MIS navigationTiRobot166136Rampersaud classification; op time; blood loss; Kim classification; LOS; complications
Feng et al., 202027China2017–2018RCTOpen fluoroscopyTiRobot4040Gertzbein-Robbins grade; op time; blood loss; drainage output; VAS score; ODI score; complications
Le et al., 202032China2015–2019Retrospective cohortOpen fluoroscopyTiRobot4623Facet joint violation
Yang et al., 202054US2011–2017Retrospective cohortNRVarious2,5282,528Complications; revision rate
Khan et al., 202045US2017–2018Retrospective cohortMIS navigationMazor X Stealth1822Ravi grade; blood loss; LOS; opioid use; learning curve
Jiang et al., 202052US2017–2019Retrospective cohortOpen freehandExcelsiusGPS2828Gertzbein-Robbins grade; blood loss; LOS; complications; readmission; pain score; learning curve
Mao et al., 202048US2017–2019Retrospective cohortOpen/MIS navigationMazor X Stealth4639Gertzbein-Robbins grade; blood loss; op time; wound healing
Fayed et al., 202051US2019–2020Retrospective cohortMIS fluoroscopyExcelsiusGPS2820Gertzbein-Robbins grade; learning curve
Jamshidi et al., 202119US2014–2019Retrospective cohortMIS fluoroscopyRenaissance111374Radiation time; op time
Katsevman et al., 202149US2011–2019Retrospective cohortMIS fluoroscopyMazor X Stealth2017Gertzbein-Robbins grade; facet violation
Chen et al., 202133China2018–2019Retrospective cohortOpen freehandTiRobot5252Gertzbein-Robbins grade; op time; LOS; blood loss; radiation time; revision rate; ODI score; VAS score; learning curve; complications
Liounakos et al., 202122US2014–2019Retrospective cohortMIS fluoroscopyRenaissance111374Complications; blood loss; op time; revision rate
Wang et al., 202121China2017–2020Retrospective cohortOpen fluoroscopyRenaissance3131Gertzbein-Robbins grade; facet violation; op time; LOS; blood loss; radiation time; hospital cost; complications
Total5,3835,347

MIS = minimally invasive surgery; NR = not reported; ODI = Oswestry Disability Index; VAS = visual analog scale.

TABLE 2.

Summary of the studies included in this systematic review involving ARSN spine surgery

Authors & YearCountryStudy PeriodStudy DesignControl GroupAR TypeControl, nARSN, nOutcome Measures
Yoon et al., 201757US2014–2015Prospective cohortNavigationGoogle Glass610Screw placement time; surgeon survey
Carl et al., 201961GermanyNRProspective cohortNAMicroscope HUDNA10Feasibility
Carl et al., 201960Germany2018Prospective cohortNAMicroscope HUDNA10Neurological outcome; radiation exposure; VAS score
Elmi-Terander et al., 201964Sweden2017Prospective cohortNAMonitor basedNA20Gertzbein-Robbins grade; screw placement time
Carl et al., 201959Germany2018–2019Prospective cohortNAMicroscope HUDNA10Feasibility
Carl et al., 202058Germany2018–2019Prospective cohortNAMicroscope HUDNA42Feasibility; radiation exposure
Burström et al., 202067Sweden2017Retrospective cohortNAMonitor basedNA20Accuracy
Edström et al., 202065SwedenNRProspective cohortNAMonitor basedNA20Op time; op flow
Gu et al., 202055China2017–2018RCTOpen fluoroscopyHoloLens II2525Gertzbein-Robbins grade; ODI score; VAS score; blood loss; op time; radiation exposure
Edström et al., 202066SwedenNRProspective cohortOpen freehandMonitor based2915Instrument density; deformity correction; LOS; blood loss; op time
Edström et al., 202063SwedenNRProspective cohortNAMonitor basedNA20Radiation exposure
Elmi-Terander et al., 202062SwedenNRProspective cohortOpen freehandMonitor based2020Gertzbein-Robbins grade; LOS; blood loss; op time
Matsukawa et al., 202156Japan2019–2020RCTMIS fluoroscopyPicoLinker smart glasses1010Cortical breach; radiation exposure; blood loss; op time
Charles et al., 202168France2019Retrospective cohortNAMonitor basedNA20Gertzbein-Robbins grade; facet violation; radiation time/exposure; op time; learning curve
Yahanda et al., 202170US2020–2021Retrospective cohortNAxvision NA9Gertzbein-Robbins grade; Watanabe classification
Liu et al., 202169US2020Retrospective cohortNAxvision NA32Gertzbein-Robbins grade
Total90293

HUD = heads-up display; NA = not applicable.

The studies analyzed in this work included retrospective cohort studies (54.7%, n = 35), RCTs (23.4%, n = 15), and prospective observational/cohort studies (21.9%, n = 14). The top 5 countries from which the studies originated were China (28.1%, n = 18), US (25.0%, n = 16), Germany (15.6%, n = 10), South Korea (9.4%, n = 6), and Sweden (9.4%, n = 6). Robotic systems used in these studies include the Renaissance (Mazor Robotics) (33.3%, n = 16),722 TiRobot (Tinavi) (22.9%, n = 11),2333 SpineAssist (Mazor Robotics) (20.8%, n = 10),3443 ROSA (Zimmer Biomet) (2.1%, n = 1),44 Mazor X Stealth (Medtronic) (10.4%, n = 5),4549 Orthobot (Sunshine Mako) (2.1%, n = 1),50 and ExcelsiusGPS (Globus Medical) (4.2%, n = 2)51,52 (Fig. 2A and B); 2 studies used various robotic systems.53,54 AR interfaces used in the studies included smart glasses head-mounted display (18.8%, n = 3),5557 microscope heads-up display AR (25.0%, n = 4),5861 nonspecified monitor-based ARSN (43.8%, n = 7),6268 and xvision (Augmedics) (12.5%, n = 2)69,70 (Fig. 2C).

FIG. 2.
FIG. 2.

A: The ExcelsiusGPS being used for preoperative planning of lumbosacral instrumentation. B: Intraoperatively, the robot is used to perform semiautonomous screw placement using preplanned placement trajectories. C: The xvision ARSN system using an integrated head-mounted optical tracking camera and transparent near-eye display shown here being used on a saw-bone spine model. This system allows the surgeon to perform instrumentation without turning the head away from the operating field. Figure is available in color online only.

Overall, 20 (41.7%) of the 48 robotic studies had matched surgical approaches (minimally invasive or open) between control and interventional arms. These included 17 retrospective cohort studies, 2 prospective studies, and 1 RCT.1422,28,30,34,37,4246,49,51 Eight studies15,16,20,28,30,34,42,44 directly compared open midline surgical techniques, and 12 studies directly compared minimally invasive percutaneous approaches.14,1719,21,22,37,43,45,46,49,51 The other studies (28/48, 58.3%) did not have similar surgical approaches between interventional and control arms.

Risk-of-Bias Assessment

For nonrandomized studies, Newcastle-Ottawa Scale scores ranged from 6 to 9, with a mean score of 7.9. A summary of the risk-of-bias assessment for nonrandomized studies is provided in Supplementary Table 1. All 13 robotic RCTs711,13,2528,35,36,50 included claimed randomization, but the method of random sequence generation was not reported in 3 of the studies, which were therefore graded as unclear risk of bias.25,35,36 Three studies did not report all incomplete outcome data.10,13,35 Three studies did not report blinding of outcome assessment,7,13,35 but blinding of participants and personnel did not occur in any of the studies, so they were therefore scored as high risk. Risk of other biases was low across all 13 studies. A summary of the bias assessment for RCTs is present in Supplementary Table 2.

Meta-Analysis

Screw Inaccuracy

All studies assessing accuracy demonstrated higher accuracy rates with robotic guidance versus conventional techniques, except for 5 studies, none of which found the difference to be significant.9,36,43,51,52 Thirty-six studies were included in the pooled analysis (7774 robotic screws and 10,502 conventional screws).79,12,14,15,18,20,2331,3346,48,5052 In studies assessing accuracy in which Gertzbein-Robbins grading was not used, other classifications were used, including the Neo classification,18 Ravi grade,45,46 Rampersaud classification,31,34 unspecified grading system,16 or facet violation classifications.11,29,31 For analysis, studies were separated based on study design (RCT vs non-RCT). Pooled analysis from 10 RCTs demonstrated more accurate (Gertzbein-Robbins grade A or B) screw placement with robotic assistance, with 24 (1.5%) of 1624 inaccurate (Gertzbein-Robbins grades C–E) screw placements in the robot-assisted group and 54 (3.1%) of 1741 in the conventional group (risk ratio [RR] 0.51, 95% CI 0.32–0.80; p = 0.004) (Fig. 3A). Pooled analysis from 26 retrospective cohort studies also demonstrated more accurate (Gertzbein-Robbins grade A or B) screw placement with robotic assistance, with 233 (3.8%) of 6188 inaccurate (Gertzbein-Robbins grades C–E) screw placements in the robot-assisted group and 626 (7.0%) 8999 in the conventional group (RR 0.51, 95% CI 0.44–0.59; p < 0.0001) (Fig. 3B). The I2 index was determined to be 60% and 49% for RCT and non-RCT pooled analysis, respectively, suggesting a moderate degree of study heterogeneity. Visual inspection of the screw inaccuracy funnel plot (Supplementary Fig. 1) revealed minimal publication bias.

FIG. 3.
FIG. 3.

Forest plots showing the RR of screw inaccuracy, substratified by studies with an RCT design (A) and studies with a retrospective cohort design (B). The squares and horizontal lines correspond to the study-specific RRs and 95% CIs of inaccurate screw placement in robot instrumentation versus controls (freehand or fluoroscopy assisted). The diamond represents the pooled RR and 95% CI of the overall population. M-H = Mantel-Haenszel. Figure is available in color online only.

Subgroup sensitivity analysis revealed that robotic instrumentation was associated with a lower risk of inaccurate screw placement regardless of if a true freehand approach (8 studies; RR 0.34, 95% CI 0.20–0.56; p < 0.0001) (Fig. 4A),8,9,20,30,33,34,44,52 a fluoroscopy-guided approach (23 studies; RR 0.56, 95% CI 0.47–0.66; p < 0.0001) (Fig. 4B),7,12,14,18,21,2329,3540,42,43,4951 or frameless stereotactic image-guided navigation (7 studies; RR 0.51, 95% CI 0.38–0.68; p < 0.0001) was performed in the control group (Fig. 4C).15,31,38,42,45,46,48

FIG. 4.
FIG. 4.

Forest plots showing the RR of screw inaccuracy, substratified by control groups, including freehand surgery control (A), fluoroscopy-guided surgery control (B), and image-guided navigation (C). The squares and horizontal lines correspond to the study-specific RRs and 95% CIs in robotic instrumentation versus controls (freehand or fluoroscopy assisted). The diamond represents the pooled RR and 95% CI of the overall population. Figure is available in color online only.

Reoperation

Nineteen studies were combined in this pooled analysis (1284 patients with robotic instrumentation and 1536 patients with conventional instrumentation).7,8,1115,2224,26,29,30,33,36,38,4143 It was found that patients undergoing robot-assisted surgery had, on average, a lower rate of revision compared with controls, with the total number of revisions totaling 37 (2.9%) in the robotic group and 152 (9.9%) in the control group (RR 0.35, 95% CI 0.25–0.50; p < 0.0001) (Fig. 5A). The I2 value was calculated to be 0%, indicating a low degree of study heterogeneity. Visual analysis of the funnel plot (Supplementary Fig. 2A) concluded that publication bias played a minimal role in influencing the results of the reported data.

FIG. 5.
FIG. 5.

Forest plot showing the RR of surgical reoperation rates (A) and perioperative complications (B). The squares and horizontal lines correspond to the study-specific RRs and 95% CIs in robotic instrumentation versus controls (freehand or fluoroscopy assisted). The diamond represents the pooled RR and 95% CI of the overall population. Figure is available in color online only.

Perioperative Complications

Twenty studies were combined in this pooled analysis (1489 patients with robotic instrumentation and 1507 patients receiving control instrumentation).7,12,15,2022,24,27,29,31,33,37,38,4043,50,52,53 It was found that the use of robotic instrumentation resulted in a lowered risk of perioperative complications compared with control methods (RR 0.72, 95% CI 0.62–0.85; p < 0.0001) (Fig. 5B). The I2 value was calculated to be 81%, indicating a very moderate to high degree of study heterogeneity. Visual analysis of the funnel plot (Supplementary Fig. 2B) concluded that publication bias played a minimal role in influencing the results of the reported data.

Estimated Blood Loss

Seventeen studies (1338 patients with robotic instrumentation and 1223 with conventional instrumentation) were combined in the pooled analysis of EBL.2022,24,2628,30,31,33,38,4548,50,52 With these data, it was found that the EBL with robotic instrumentation was, on average, 89.1 mL (95% CI 39.1–139.1 mL; p = 0.0005) lower than it was with conventional instrumentation (Supplementary Fig. 3A). This metric was limited by nonrandom data heterogeneity (I2 = 96%, Supplementary Fig. 4A).

Intraoperative Time

Twenty-nine studies (2047 patients with robotic instrumentation and 1727 with conventional instrumentation) were combined in the pooled analysis of IOT.8,9,12,15,1931,33,3538,40,43,44,4648,50 Pooled analysis found that surgeries with robotic instrumentation were longer than surgeries with control instrumentation (+24.32 minutes, 95% CI 11.27–37.37 minutes; p = 0.0003) (Supplementary Fig. 3B). This metric was limited by a high degree of nonrandom data heterogeneity (I2 = 95%) and studies appearing outside the pseudo–95% CI of the publication bias funnel plot (Supplementary Fig. 4B).

Postoperative LOS

Eighteen studies (1186 patients with robotic instrumentation and 1633 with conventional instrumentation) were combined in the pooled analysis of postoperative LOS.7,15,21,23,24,26,28,30,31,33,38,43,4547,50,52,53 With these data, it was found that patients who received surgery with robotic instrumentation had a statistically significant decrease in their postoperative LOS (−1.2 days, 95% CI −1.7 to −0.6 days; p < 0.0001) (Supplementary Fig. 3C). This metric was limited by a high degree of nonrandom data heterogeneity (I2 = 92%). Funnel plot analysis revealed minimal publication bias with respect to this metric (Supplementary Fig. 4C).

Radiation Exposure

Twenty studies (1389 patients with robotic instrumentation and 1361 with conventional instrumentation) were combined in the pooled analysis of duration of radiation exposure.7,12,15,20,21,23,24,26,29,3336,38,40,41,43,44,46,50 It was found that there was no statistically significant decrease in the duration of radiation exposure to the patient using robotic instrumentation compared with control instrumentation (mean difference 1.96 seconds, 95% CI −2.94 to 6.87 seconds; p = 0.43) (Supplementary Fig. 3D). Data heterogeneity was significantly elevated (I2 = 98%), suggesting that the amount of radiation varied significantly among different studies. Publication bias was also significantly elevated, as suggested by the majority of studies falling outside of the pseudo–95% CIs of the duration of radiation exposure funnel plot (Supplementary Fig. 4D).

Learning Curve

Seven studies quantified the effects of a learning curve associated with robotic instrumentation compared with control instrumentation.7,9,33,36,45,51,52 This analysis took the form of performing a linear regression analysis, comparing the first number of cases with the last number of cases for a given parameter, then quantifying any difference via statistical significance testing. Two studies found no statistically significant changes in outcome parameters when stratified by "early" versus "late" cases.36,45 Three studies described a statistically significant decrease in IOT as a function of number of cases performed,7,9,52 and 1 study reported a decrease in the IOT variance as a function of number of cases performed.33 One study additionally found a decrease in the duration of radiation exposure as a function of the number of cases performed.7 Finally, 1 study reported differences in screw accuracy as a function of number of cases performed.51

ARSN Versus Robotic and Conventional Techniques

The quality of data yielded from studies describing ARSN was generally low, with the design of the majority of these studies being either single-arm cohorts or case studies. Only 2 studies55,56 in this systematic review were RCTs. The analyzed data regarding EBL, screw accuracy, radiation dose or exposure, registration error (as measured in mm), and postoperative LOS are summarized in Table 3. With respect to EBL, patients receiving ARSN had similar EBL compared with patients undergoing robot-assisted or conventional instrumentation (420.3 mL vs 249.8 mL vs 465.8 mL, respectively; p = 0.3375) (Fig. 6A). Regarding postoperative LOS, there were no significant differences in using ARSN versus robotic and conventional instrumentation (Fig. 6B). When comparing ARSN with robotic and conventional instrumentation with respect to screw accuracy, only 1 study55 was sufficiently powered to capture differences in inaccurate screw placement. In this study, it was found that screws placed with conventional instrumentation had a 1.75-fold greater risk of being placed inaccurately relative to placement with ARSN assistance (95% CI 0.28–1.02; p = 0.0721). Single-cohort studies without control groups also reported between 93.9% and 100.0% of screws placed with ARSN attaining a Gertzbein-Robbins grade A accuracy scoring.55,62,64,6870 Another significant finding was that the use of ARSN resulted in a significantly decreased radiation dose when compared with both robotic instrumentation (p = 0.0091) and fluoroscopy-guided conventional instrumentation (p < 0.0001) (Fig. 6C). The mean registration error reported among 5 studies5861,67 was calculated to be 1.07 ± 0.35 mm.

TABLE 3.

Operative variables and perioperative outcomes extracted from studies describing patients undergoing ARSN-assisted spine procedures

ARSNControlReported p value
No.MeanNo.Mean
EBL (mL)
 Edström et al., 20206615670 ± 423291,306 ± 1,032<0.01
 Elmi-Terander et al., 20206220628 ± 386201,165 ± 1,103NS
 Matsukawa et al., 2021561055.1 ± 35.11066.3 ± 38.3NS
 Gu et al., 20205525382.27 ± 95.7525449.76 ± 91.690.0142
Op time (mins)
 Edström et al., 20206520403 ± 101NANANA
 Gu et al., 2020552596.0 ± 11.9325120.1 ± 13.14<0.001
 Edström et al., 20206615431 ± 9829417 ± 145NS
 Elmi-Terander et al., 20206220403 ± 10120361 ± 150NS
 Matsukawa et al., 20215610100.2 ± 10.410105.5 ± 14.6NS
 Charles et al., 20216820117 ± 11NANANA
Radiation dose or time
 Carl et al., 201961 (mSv)100.95 ± 0.3NANANA
 Carl et al., 201960 (mSv)104.54 ± 1.3NANANA
 Carl et al., 201959 (mSv)103.38 ± 2.2NANANA
 Carl et al., 202058 (mSv)422.24 ± 1.1NANANA
 Edström et al., 202063 (mSV) 2015.8 ± 1.8NANANA
 Matsukawa et al., 202156 (sec)1038.6 ± 6.61041.8 ± 16.1NS
 Gu et al., 202055 (times)255.76 ± 0.83256.60 ± 1.290.0092
 Charles et al., 202168 (Gy/cm2) 2035.9 ± 15.5NANANA
 Charles et al., 202168 (sec) 2022.5 ± 10.1NANANA
Registration error (mm)
 Carl et al., 201961101.11 ± 0.42NANANA
 Carl et al., 20196010"About 1.0"NANANA
 Carl et al., 201959100.72 ± 0.24NANANA
 Carl et al., 202058420.87 ± 0.2 NANANA
 Burström et al., 202067201.65 ± 1.2NANANA
Postoperative LOS (days)
 Edström et al., 202066155.5 ± 1.8298.2 ± 3.6<0.01
 Elmi-Terander et al., 202062205.3 ± 1.7206.8 ± 3.6NS
Inaccurate screw placement*Total No.No. of Events (%)Total No.No. of Events (%)Reported p value
 Elmi-Terander et al., 20196425315 (5.9)NANANA
 Elmi-Terander et al., 20206226216 (6.1)28830 (10.4)0.05
 Gu et al., 2020551426 (4.2)13815 (10.9) 0.0348
 Matsukawa et al., 202156400 (0.0)401 (2.5)NS
 Charles et al., 202168805 (6.2)NANANA
 Yahanda et al., 202170632 (3.2)NANANA
 Liu et al., 2021692054 (2.0)NANANA

NS = not significant.

Defined as Gertzbein-Robbins grade > 2/B unless otherwise specified.

FIG. 6.
FIG. 6.

Patient-centered operative and perioperative outcomes of patients undergoing AR-guided spinal instrumentation procedures compared with robot-assisted and conventional techniques. A: There was no statistically significant difference in EBL in patients receiving ARSN versus patients receiving robot-assisted or conventional instrumentation. B: There were no statistically significant differences in the postoperative LOS. C: There was a statistically significant decrease in the dosage of radiation in procedures involving ARSN when compared with both robotic instrumentation (p = 0.0091) and fluoroscopy-guided instrumentation (p < 0.0001). Bars denote mean and standard error of the mean. **p < 0.01; ****p < 0.0001; ns = not significant.

Discussion

Technology-enhanced instrumentation in spinal surgery has the capacity to greatly improve patient outcomes. The importance of this technology is clear in the fact that multiple recent other meta-analyses have attempted to summarize the data on robot-assisted spinal surgery (Supplementary Table 3).7180 To our knowledge, this report is the largest comparison to date of patient-centered outcomes with respect to robotic versus conventional instrumentation. As the technology is fine-tuned and access to robotic guidance increases in spinal surgery, use of these advancements in instrumentation will become even more relevant in pathologies beyond degenerative spinal disease, including procedures for deformity,66 trauma, infection,12,41 and neoplastic59 conditions of the spine.

A single previous large-scale meta-analysis investigated screw accuracy as the primary outcome; however, it did not incorporate patient-centered outcomes in its analysis.77 The data in our meta-analysis showed that robot-assisted surgery is associated with not only a lower risk of inaccurate screw placement but also a lower risk of both perioperative complications and surgical reoperation relative to conventional instrumentation. This is congruent with prior meta-analyses comparing similar cohorts of studies; however, our study shows smaller effect sizes compared with what others have reported, likely due to the increased number of studies included in this work.72,76 We also found that robotic surgery may be associated with lower EBL, lower postoperative LOS, and increased operative time compared with conventional instrumentation, but publication biases and nonrandom data heterogeneity (I2 > 75%) (Supplementary Fig. 4) significantly limit the validity of these conclusions.

Due to its ability to integrate vast amounts of information onto the surgeon’s field of vision, ARSN is yet another means by which technology can be harnessed to improve patient care and outcomes. Like robotic instrumentation, ARSN has the capacity to improve screw placement accuracy over both freehand and fluoroscopy-guided techniques, but no studies to date have directly compared the accuracy rates of robotic instrumentation versus AR-guided instrumentation. The only patient-centered advantage for ARSN yielded in this study was a decrease in the amount of radiation exposure (Fig. 6C). Additional purported advantages of ARSN over robotics are decreased line-of-sight interruption and attention shift, allowing the surgeon to focus on the patient’s pathology and not the computer screen with a head-mounted display or microscope-mediated display (Fig. 2C). For better comparisons to take place among ARSN, robotic instrumentation, and conventional methods, more objective data and higher-quality studies are needed.

Given the findings of improved screw accuracy placement and decreased rates of surgical revision, technology-enhanced instrumentation for surgical procedures may seem like an attractive option for institutions; however, several limitations to this technology exist. First, due to the high startup and maintenance cost, this technology is inevitably linked to high-volume neurosurgical services in the developed world. Given that much of the global neurosurgical burden is spine based and geographically located in regions where a $1,000,000 investment in a robotic system is not fiscally possible, concerns arise as to how to make this technology globally available in the most equitable manner possible. Nonetheless, only 1 study in our review analyzed direct costs to the hospital and showed no difference between robot-assisted and conventional lumbar interbody fusions.47 Further studies on cost-effectiveness of robotic and ARSN systems are warranted.

Second, one must consider the learning curve that exists with any technology. Both robotic and AR-enabling technologies may provide workflow disruptions that require time and experience to overcome. Six studies included in this review demonstrate that a learning curve may exist and can be overcome. While some studies36,45 have demonstrated no significant difference between the first half and second half of cases in their series, Fayed et al.51 presented an increase in accuracy rates with robot-guided instrumentation over this time period. Additional studies7,9,52 demonstrated decreased operative time and fluoroscopy per screw with time and experience. Overall, the surgeon’s experience with spine surgery does not affect the ability to adopt these technologies. As robotic and ARSN-guided systems are incorporated into use, they may augment surgical skills for trainees and help overcome any learning curves.

Finally, the selection bias when determining candidates for robotic or AR assistance should be addressed. As noted, most robotic studies in our review (28/48; 58.3%) utilized a minimally invasive percutaneous approach with robotic intervention but an open midline approach in the control arm or a mix of the two approaches. Prior literature demonstrates the advantages of minimally invasive spine surgery alone without the use of robotics, including reduced blood loss, hospital LOS, and perioperative surgical complications. Future studies should aim to have both control and interventional approaches be identical so a more robust analysis on the benefits of robot-guided surgery can be ascertained. Additionally, candidates for robotic surgery typically are those requiring elective procedures and are overall healthier than patients requiring emergency surgery. Candidates for robotic surgery typically undergo their operation during normal operating hours, with dedicated technologists in the same operating room as the attending surgeon during the case. The efficacy of the technology is questioned when employed outside of these specific environmental parameters, which is an inherent limitation toward making this technology more affordable and available to a wider population of patients requiring spinal instrumentation.

Limitations

Most of the studies analyzed in this meta-analysis are observational and retrospective in nature and thus are of lower quality than the gold standard of an RCT. Therefore, the effects of selection bias when enrolling patients into studies comparing robot-assisted versus controls cannot be fully ascertained. Publication bias and nonrandom data heterogeneity were discovered in this meta-analysis with many patient-centered clinical outcomes, which significantly limit any conclusions with the available pooled data. Likewise, the higher accuracy rates do not mean that conventional techniques, which are used in most practices, are less effective. Furthermore, a number of studies in our meta-analysis had financial disclosures that may be perceived as conflicts of interest.10,14,39,40,44

Significantly fewer publications explored ARSN, and these studies were also affected by publication and selection bias, as many of these studies were single-surgeon, single-institution experiences. There were also no direct comparisons between ARSN and robotic instrumentation, thus making definitive conclusions regarding these data difficult. Finally, variances in surgical technique are not described in many cases, and robot and ARSN technologies may differ among each other. Both limitations represent another layer of potential confounding in the results reported.

Conclusions

We report the largest meta-analysis to date comparing surgical outcomes between robot-assisted and conventional instrumentation in patients undergoing spinal surgery as well as a preliminary survey of data exploring screw accuracy and differences in patient-centered outcomes when utilizing ARSN. Patients undergoing either robot-assisted surgery or surgery with ARSN supplementation have lower risks of inaccurate screw placement and reoperation. Additionally, there is evidence that robotic assistance also contributes to lower perioperative complications, LOS, and EBL. However, there are not yet enough data to draw significant conclusions, and this is further limited by biases. Factors such as global/fiscal feasibility and a narrow window of patient selection need to be addressed for this technology to advance beyond its current state. As these technologies expand and become more prevalent in healthcare institutions across the globe, and as the momentum of data and evidence available is carried through the next decade, the expectation is for more patients to benefit from robotic assistance and ARSN-guided spinal instrumentation.

Disclosures

Dr. Sandhu: royalties from Stryker and Globus.

Author Contributions

Conception and design: Dowlati, Voyadzis. Acquisition of data: Dowlati, Zhao, Khan, Pasko. Analysis and interpretation of data: Dowlati, Tovar, Zhao, Khan, Pasko. Drafting the article: Dowlati, Tovar, Khan, Pasko. Critically revising the article: Dowlati, Tovar, Zhao, Sandhu, Voyadzis. Reviewed submitted version of manuscript: Dowlati, Tovar, Zhao, Pasko, Sandhu, Voyadzis. Approved the final version of the manuscript on behalf of all authors: Dowlati. Statistical analysis: Tovar. Study supervision: Sandhu, Voyadzis.

Supplemental Information

Online-Only Content

Supplemental material is available with the online version of the article.

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  • Collapse
  • Expand

Images from Zhou et al. (pp 274–282).

  • FIG. 1.

    A: Bar graph of PubMed search results of studies involving robotic instrumentation (gray) and ARSN (yellow) for spinal surgery over the last decade. B: PRISMA flow diagram of the systematic literature search for this systematic review and meta-analysis. Data added to the PRISMA template [from Moher D, Liberati A, Tetzlaff J, Altman DG, The PRISMA Group (2009). Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. PLoS Med. 6(7):e1000097] under the terms of the Creative Commons Attribution License. Figure is available in color online only.

  • FIG. 2.

    A: The ExcelsiusGPS being used for preoperative planning of lumbosacral instrumentation. B: Intraoperatively, the robot is used to perform semiautonomous screw placement using preplanned placement trajectories. C: The xvision ARSN system using an integrated head-mounted optical tracking camera and transparent near-eye display shown here being used on a saw-bone spine model. This system allows the surgeon to perform instrumentation without turning the head away from the operating field. Figure is available in color online only.

  • FIG. 3.

    Forest plots showing the RR of screw inaccuracy, substratified by studies with an RCT design (A) and studies with a retrospective cohort design (B). The squares and horizontal lines correspond to the study-specific RRs and 95% CIs of inaccurate screw placement in robot instrumentation versus controls (freehand or fluoroscopy assisted). The diamond represents the pooled RR and 95% CI of the overall population. M-H = Mantel-Haenszel. Figure is available in color online only.

  • FIG. 4.

    Forest plots showing the RR of screw inaccuracy, substratified by control groups, including freehand surgery control (A), fluoroscopy-guided surgery control (B), and image-guided navigation (C). The squares and horizontal lines correspond to the study-specific RRs and 95% CIs in robotic instrumentation versus controls (freehand or fluoroscopy assisted). The diamond represents the pooled RR and 95% CI of the overall population. Figure is available in color online only.

  • FIG. 5.

    Forest plot showing the RR of surgical reoperation rates (A) and perioperative complications (B). The squares and horizontal lines correspond to the study-specific RRs and 95% CIs in robotic instrumentation versus controls (freehand or fluoroscopy assisted). The diamond represents the pooled RR and 95% CI of the overall population. Figure is available in color online only.

  • FIG. 6.

    Patient-centered operative and perioperative outcomes of patients undergoing AR-guided spinal instrumentation procedures compared with robot-assisted and conventional techniques. A: There was no statistically significant difference in EBL in patients receiving ARSN versus patients receiving robot-assisted or conventional instrumentation. B: There were no statistically significant differences in the postoperative LOS. C: There was a statistically significant decrease in the dosage of radiation in procedures involving ARSN when compared with both robotic instrumentation (p = 0.0091) and fluoroscopy-guided instrumentation (p < 0.0001). Bars denote mean and standard error of the mean. **p < 0.01; ****p < 0.0001; ns = not significant.

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