Surgeons' Learning Curve of Renaissance Robotic Surgical System

Safety of robotic-assisted screw placement for spine surgery: Experience from the initial 125 cases

BACKGROUND

The present study aimed to evaluate the safety of robot-assisted screw placement in 125 cases after introducing a spinal robotics system and to identify the situations where deviation was likely to occur.

METHODS

The subjects were 125 consecutive patients who underwent robotic-assisted screw placement using a spinal robotics system (Mazor X Stealth Edition, Medtronic) from April 2021 to January 2023. The 1048 screws placed with robotic assistance were evaluated. We investigated intraoperative adverse events of the robotics system and complications occurring within 30 days after surgery. We evaluated screw accuracy and deviation and compared them for vertebral levels, screw insertion methods (open traditional pedicle screw [Open-PS], cortical bone trajectory screw [CBT], percutaneous pedicle screw [PPS], and S2 alar iliac screw [S2AIS]), diagnosis, and phases of surgical cases.

RESULTS

The deviation rate of robotic-assisted screw placement for spine surgery was 2.2%. Complications were reoperation due to implant-related neurological deficit in 0.8% and surgical site infection in 0.8%. There was significant difference in the deviation rate between vertebral levels. The deviation rate of the T1-T4 level was high at 10.0%. There was significant difference in the deviation rate between Open-PS, CBT, PPS, and S2AIS. The PPSs had a high deviation rate of 10.3%. The deviation rates were not significantly different between patients with and without deformity. The deviation rate did not change depending on the experience of surgical cases, and the deviation rate was favorable from the onset.

CONCLUSION

Although the robotic-assisted screw placement was safe, we should be extra vigilant when placing screws in the upper thoracic region (deviation rate 10.0%) and when using PPSs (deviation rate 10.3%).

Learning curves for robotic-assisted spine surgery: an analysis of the time taken for screw insertion, robot setting, registration, and fluoroscopy

PURPOSE

The purpose of this study was to clarify the learning curve for robotic-assisted spine surgery. We analyzed the workflow in robotic-assisted spine surgery and investigated how much experience is required to become proficient in robotic-assisted spine surgery.

METHODS

The data were obtained from consecutive 125 patients who underwent robotic-assisted screw placement soon after introducing a spine robotic system at a single center from April 2021 to January 2023. The 125 cases were divided into phases 1-5 of sequential groups of 25 cases each and compared for screw insertion time, robot setting time, registration time, and fluoroscopy time.

RESULTS

There were no significant differences in age, body mass index, intraoperative blood loss, number of fused segments, operation time, or operation time per segment between the 5 phases. There were significant differences in screw insertion time, robot setting time, registration time, and fluoroscopy time between the 5 phases. The screw insertion time, robot setting time, registration time, and fluoroscopy time in phase 1 were significantly longer than those in phases 2, 3, 4, and 5.

CONCLUSION

In an analysis of 125 cases after the introduction of the spine robotic system, the screw insertion time, robot setting time, registration time, and fluoroscopy time were significantly longer in the 25 cases in the period initially after introduction. The times were not significantly different in the subsequent 100 cases. Surgeons can be proficient in robotic-assisted spine surgery after their experience with 25 cases.

Learning curve of junior surgeons in robot-assisted pedicle screw placement: a comparative cohort study

OBJECTIVE

Robot-assisted technology has been gradually applied to pedicle screw placement in spinal surgery. This study was designed to detailedly evaluate the learning curve of junior surgeons in robot-assisted spine surgery.

METHODS

From December 2020 to February 2022, 199 patients requiring surgical treatment with posterior pedicle screw fixation were prospectively recruited into the study. The patients were randomized to the robot-assisted group (the RA group) or the conventional freehand group (the CF group). Under the senior specialist's supervision, pedicle screws were placed by two junior fellows without prior experience. Cumulative summation (CUSUM) analysis was performed on the learning curve of pedicle screw placement for performing quantitative assessment based on the time of screw insertion.

RESULTS

In total, 769 and 788 pedicle screws were placed in the RA and CF groups. Compared with the CF group, the learning duration in the RA group was shorter in the upper thoracic region (57 vs. 70 screws), but longer in the lower thoracic (62 vs. 58 screws) and the lumbosacral region (56 vs. 48 screws). The slope of learning curve was lower in the RA group than in the CF group. The screw accuracy in the RA group was superior to that in the CF group, especially in upper thoracic region (89.4% vs. 76.7%, P < 0.001). This disparity of accuracy became wider in deformity cases. In the upper thoracic region, the mean placement time was 5.34 ± 1.96 min in the RA group and 5.52 ± 2.43 min in the CF groups, while in the lower thoracic and lumbosacral regions, the CF group's mean placement times were statistically shorter. Three screw-related neural complications occurred in the CF group.

CONCLUSION

Robot-assisted technique has its advantages in the upper thoracic region and deformity cases, which is easier and safer to insert pedicle screws. The robot-assisted technique allowed a short learning curve for junior surgeons and exhibited consistently excellent results even in the early application period.

Spine Surgical Robotics: Current Status and Recent Clinical Applications

With the development of artificial intelligence and the further deepening of medical-engineering integration, spine surgical robot-assisted (RA) technique has made significant progress and its applicability in clinical practice is constantly expanding in recent years. In this review, we have systematically summarized the majority of literature related to spine surgical robots in the past decade, and not only classified robots accordingly, but also summarized the latest research progress in RA technique for screw placement such as cervical, thoracic, and lumbar pedicle screws, cortical bone trajectory screws, cervical lateral mass screws, and S2 sacroiliac screws; guiding targeted puncture and placement of endoscope via the intervertebral foramen; complete resection of spinal tumor tissue; and decompressive laminectomy. In addition, this report also provides a detailed evaluation of RA technique's advantages and disadvantages, and clarifies the accuracy, safety, and practicality of RA technique. We consider that this review can help clinical physicians further understand and familiarize the current clinical application status of spine surgical robots, thereby promoting the continuous improvement and popularization of RA technique, and ultimately benefiting numerous patients.

Accuracy of robotic-assisted pedicle screw placement comparing junior surgeons with expert surgeons: Can junior surgeons place pedicle screws as accurately as expert surgeons?

BACKGROUND

The purpose of this study was to verify whether a spine robotic system was useful for junior surgeons.

METHODS

Twenty-seven patients underwent posterior spinal fusion with open surgery using a spine robotic system (Mazor X Stealth Edition, Medtronic Inc., Dublin, Ireland) from April to August 2021. Pedicle screw insertions were performed by five surgeons. The surgeon and insertion time were recorded for each pedicle screw. Two surgeons who are board-certified spine surgeons by the Japanese Society for Spine Surgery and Related Research (JSSR) were defined as the expert surgeon group. Three surgeons who were training to acquire qualifications as JSSR board certified spine surgeons were defined as the junior surgeon group. In postoperative CT images, the deviation of 255 pedicle screws was evaluated using the Gertzbein-Robbins (GR) grades.

RESULTS

In the expert surgeon group, the GR grades were Grade A for 79 screws (90.8%), Grade B for 6 (6.9%), Grade C for 2 (2.3%), and 0 (0%) for Grades D and E. I In the junior surgeon group, the GR grades were Grade A for 162 screws (96.4%), Grade B for 6 (3.6%), and 0 (0%) for Grades C, D, and E. There was no significant difference in the deviation rate between surgeon groups (p = 0.08). The mean insertion times were 174.5 ± 83.0 s in the expert surgeon group and 191.0 ± 111.0 s in the junior surgeon group. There was no significant difference in the insertion time between surgeon groups (p = 0.22).

CONCLUSIONS

There were no significant differences in the deviation rate and the insertion time of robotic-assisted pedicle screw placement between expert surgeons and junior surgeons who were training to acquire qualifications as JSSR board certified spine surgeons. Robotic-assisted pedicle screw placement can be effectively employed by junior surgeons.

Robotics in spine surgery: systematic review of literature

PURPOSE

Over 4.83 million spine surgery procedures are performed annually around the world. With the considerable caseload and the precision needed to achieve optimal spinal instrumentation, technical progress has helped to improve the technique's safety and accuracy with the development of peri-operative assistance tools. Contrary to other surgical applications already part of the standard of care, the development of robotics in spine surgery is still a novelty and is not widely available nor used. Robotics, especially when coupled with other guidance modalities such as navigation, seems to be a promising tool in our quest for accuracy, improving patient outcomes and reducing surgical complications. Robotics in spine surgery may also be for the surgeon a way to progress in terms of ergonomics, but also to respond to a growing concern among surgical teams to reduce radiation exposure.

METHOD

We present in this recent systematic review of the literature realized according to the PRISMA guidelines the place of robotics in spine surgery, reviewing the comparison to standard techniques, the current and future indications, the learning curve, the impact on radiation exposure, and the cost-effectiveness.

RESULTS

Seventy-six relevant original studies were identified and analyzed for the review.

CONCLUSION

Robotics has proved to be a safe help for spine surgery, both for the patient with a decrease of operating time and increase in pedicular screw accuracy, and for the surgical team with a decrease of radiation exposure. Medico-economic studies demonstrated that despite a high buying cost, the purchase of a robot dedicated for spine surgery is cost-effective resulting in lesser revision, lower infection, reduced length of stay, and shorter surgical procedure.

Augmented reality in minimally invasive spine surgery: early efficiency and complications of percutaneous pedicle screw instrumentation

BACKGROUND CONTEXT

Augmented reality (AR) employs an optical projection directly onto the user's retina, allowing complex image overlay on the natural visual field. In general, pedicle screw accuracy rates have improved with increasingly use of technology, with navigation-based instrumentation described as accurate in 89%-100% of cases. Emerging AR technology in spine surgery builds upon current spinal navigation to provide 3-dimensional imaging of the spine and powerfully reduce the impact of inherent ergonomic and efficiency difficulties.

PURPOSE

This publication describes the first known series of in vivo pedicle screws placed percutaneously using AR technology for MIS applications.

STUDY DESIGN / SETTING

After IRB approval, 3 senior surgeons at 2 institutions contributed cases from June, 2020 - March, 2022. 164 total MIS cases in which AR used for placement of percutaneous pedicle screw instrumentation with spinal navigation were identified prospectively.

PATIENT SAMPLE

155 (94.5%) were performed for degenerative pathology, 6 (3.6%) for tumor and 3 (1.8%) for spinal deformity.  These cases amounted to a total of 606 pedicle screws; 590 (97.3%) were placed in the lumbar spine, with 16 (2.7%) thoracic screws placed.

OUTCOME MEASURES

Patient demographics and surgical metrics including total posterior construct time (defined as time elapsed from preincision instrument registration to final screw placement), clinical complications and instrumentation revision rates were recorded in a secure and de-identified database.

METHODS

The AR system used features a wireless headset with transparent near-eye display which projects intra-operative 3D imaging directly onto the surgeon's retina. After patient positioning, 1 percuntaneous and 1 superficial reference marker are placed. Intra-operative CT data is processed to the headset and integrates into the surgeon's visual field creating a "see-through" 3D effect in addition to 2D standard navigation images. MIS pedicle screw placement is then carried out percutaneously through single line of sight using navigated instruments.

RESULTS

Time elapsed from registration and percutaneous approach to final screw placement averaged 3 minutes and 54 seconds per screw.  Analysis of the learning curve revealed similar surgical times in the early cases compared to the cases performed with more experience with the system.  No instrumentation was revised for clinical or radiographic complication at final available follow-up ranging from 6-24 months. A total of 3 screws (0.49%) were replaced intra-operatively. No clinical effects via radiculopathy or neurologic deficit postoperatively were noted.

CONCLUSIONS

This is the first report of the use of AR for placement of spinal pedicle screws using minimally invasive techniques.   This series of 164 cases confirmed efficiency and safety of screw placement with the inherent advantages of AR technologies over legacy enabling technologies.

Learning curves in robotic neurosurgery: a systematic review

The transition to performing procedures robotically generally entails a period of adjustment known as a learning curve as the surgeon develops a familiarity with the technology. However, no study has comprehensively examined robotic learning curves across the field of neurosurgery. We conducted a systematic review to characterize the scope of literature on robotic learning curves in neurosurgery, assess operative parameters that may involve a learning curve, and delineate areas for future investigation. PubMed, Embase, and Scopus were searched. Following deduplication, articles were screened by title and abstract for relevance. Remaining articles were screened via full text for final inclusion. Bibliographic and learning curve data were extracted. Of 746 resultant articles, 32 articles describing 3074 patients were included, of which 23 (71.9%) examined spine, 4 (12.5%) pediatric, 4 (12.5%) functional, and 1 (3.1%) general neurosurgery. The parameters assessed for learning curves were heterogeneous. In total, 8 (57.1%) of 14 studies found reduced operative time with increased cases, while the remainder demonstrated no learning curve. Six (60.0%) of 10 studies reported reduced operative time per component with increased cases, while the remainder indicated no learning curve. Radiation time, radiation time per component, robot time, registration time, setup time, and radiation dose were assessed by ≤ 4 studies each, with 0-66.7% of studies demonstrated a learning curve. Four (44.4%) of 9 studies on accuracy showed improvement over time, while the others indicated no improvement over time. The number of cases required to reverse the learning curve ranged from 3 to 75. Learning curves are common in robotic neurosurgery. However, existing studies demonstrate high heterogeneity in assessed parameters and the number of cases that comprise the learning curve. Future studies should seek to develop strategies to reduce the number of cases required to reach the learning curve.

Development of a Robotic Spine Surgery Program: Rationale, Strategy, Challenges, and Monitoring of Outcomes After Implementation

Surgical robots were invented in the 1980s, and since then, robotic-assisted surgery has become commonplace. In the field of spine surgery, robotic assistance is utilized mainly to place pedicle screws, and multiple studies have demonstrated that robots can increase the accuracy of screw placement and reduce radiation exposure to the patient and the surgeon. However, this may be at the cost of longer operative times, complications, and the risk of errors in mapping the patient's anatomy.

Advanced Technologies for Outpatient Lumbar Fusion: Barriers and Opportunities

BACKGROUND

In recent years, there has been increasing interest in outpatient spine surgery. Minimally invasive techniques have created an opportunity for ambulatory lumbar fusion, and these techniques increasingly involve advanced technologies such as navigation and robotics.

OBJECTIVE

To explore the barriers, advantages, and future predictions for such technology in the context of outpatient lumbar fusions.

METHODS

This is a narrative review of studies examining the advantages, limitations, and cost-effectiveness of navigation and spinal robotics in conjunction with the outcomes and costs of outpatient lumbar fusion.

RESULTS

Outpatient lumbar fusion is a growing trend with ample evidence of its safety, favorable patient outcomes, and cost savings. Navigation and spinal robotics are associated with improved instrumentation accuracy and fewer complications, and the long-term cost savings can make these technologies financially practical in the outpatient setting. Future capabilities with robotics will only increase their value.

CONCLUSIONS

Advanced technologies such as navigation and robotics are strategic long-term investments in the context of outpatient lumbar fusion.

CLINICAL RELEVANCE

The favorable outcomes and costs associated with navigation and robotics will be relevant to any spine surgeon interested in developing an outpatient lumbar fusion program.

LEVEL OF EVIDENCE: 5

The Learning Curve of Robotic-Assisted Pedicle Screw Placements Using the Cumulative Sum Analysis: A Study of the First 50 Cases at a Single Center

Introduction

The purpose of this study was to clarify how many cases surgeons need to experience to pass the learning phase of robotic-assisted spine surgery using the cumulative sum (CUSUM) analysis.

Methods

A retrospective review was conducted on the initial 50 consecutive patients who underwent robotic-assisted pedicle screw placements with open procedures using a spine robotic system (Mazor X Stealth Edition) at a single center from April 2021 to January 2022. There were 19 male and 31 female patients with a mean age of 58.7 (range, 13-86) years. To split the surgeries into the early and late phases using the CUSUM analysis of screw insertion time, we compared the screw insertion time, the robot setting time, the registration time, and the operation time in the early and late phases.

Results

The screw insertion time, the robot setting time, and the registration time declined as the number of surgical cases increased. The operation time did not decline as the number of surgical cases increased. The learning curve for screw insertion time can be separated into two stages based on the CUSUM analysis. The first 23 cases were in the early phase, and the later 27 cases were in the late phase. The mean screw insertion time was reduced from 3.2 min in the first 23 cases to 2.7 min in the subsequent 27 cases. The robot setting time and registration time in the late phase were also significantly shorter than those in the early phase.

Conclusions

The screw insertion time, robot setting time, and registration time decreased with experience. After 23 cases, surgeons passed the learning phase of robotic-assisted spine surgery and became more proficient.

Screw Insertion Time, Fluoroscopy Time, and Operation Time for Robotic-Assisted Lumbar Pedicle Screw Placement Compared With Freehand Technique

Introduction

The purpose of this study was to clarify the superiority of robotic-assisted lumbar pedicle screw placement in terms of screw insertion time, fluoroscopy time, and operation time.

Methods

The subjects were 46 patients who underwent a posterior lumbar interbody fusion with an open procedure for lumbar degenerative disease from April 2021 to February 2022. The robot group contained 29 cases of screw insertion using a spine robotic system (Mazor X Stealth Edition, Medtronic Inc., Dublin, Ireland). The freehand group contained 17 cases of screw insertion with the freehand technique utilizing the conventional C-arm image guidance. The screw insertion time, fluoroscopy time, and operation time were compared between the robot and the freehand group.

Results

The screw insertion time did not differ significantly between the two groups (robot group: 179.0 ± 65.2 sec; freehand group: 164.2 ± 83.4 sec; p = 0.507). The fluoroscopy time was significantly shorter in the robot group (robot group: 28.3 ± 25.8 sec; freehand group: 67.5 ± 72.8 sec; p = 0.011). The fluoroscopy time per segment was also significantly shorter in the robot group (robot group: 17.8 ± 23.0 sec; freehand group: 60.2 ± 74.8 sec; p = 0.007). The operation time was significantly longer in the robot group (robot group: 249.6 ± 72.5 min; freehand group: 195.8 ± 60.1 sec; p = 0.013), but the operation time per segment did not differ significantly between the two groups (robot group: 144.1 ± 39.0 min; freehand group: 159.7 ± 34.4 min; p = 0.477).

Conclusions

The screw insertion time and operation time per segment were similar when employing the spine robotic system compared to the freehand technique; however, the fluoroscopy time was shorter. The fluoroscopy time per segment in the robot group was 29.6% of the time of the freehand group using the C-arm. The surgeon's radiation exposure is thought to be decreased since the spine robotic system shortens the fluoroscopy time.

Learning Curve of ROSA ONE Spine System for Transpedicular Screw Placement

Objective

The study investigated our institutional learning curve for the ROSA ONE spine system (ROSA) based on ROSA usage time.

Methods

ROSA was designed to provide high accuracy for spinal pedicle screw placement through a built-in tracking technique. This study was conducted from November 2018 to January 2021. The time taken to complete each step of the robotic workflow was recorded. Patient demographics, comorbidities, surgical indications, and number of screw placements were examined in subgroup analysis. The Curve Fitting-General package (a part of NCSS 2021 software) was used to fit a mathematical model to the learning curve. Patient demographics, imaging data, and surgical time were reviewed retrospectively.

Results

A total of 167 patients who had undergone surgery were included. The mean total ROSA usage time was 107.1 ± 27.3 minutes. The estimated learning rate was 90.4%, and the largest slope change occurred close to the time of the 20th surgery. The observed overall learning trend in the 4-screw group could be attributed to screw planning. The presence of scoliosis (p = 0.73) or spondylolisthesis (p = 0.70) did not significantly influence the mean total time (TT) for all patients; however, the mean TT differed significantly (p < 0.01) among subgroups stratified by body mass index, screw number placement, and thoracic spine involvement.

Conclusion

To the best of our knowledge, this is the first study to examine the learning curve for the various crucial steps of ROSA-guided pedicle screw placement. The indicative learning curve involved 20 patients who had undergone surgery.

Learning curves in robot-assisted spine surgery: a systematic review and proposal of application to residency curricula

OBJECTIVE

Spine robots have seen increased utilization over the past half decade with the introduction of multiple new systems. Market research expects this expansion to continue over the next half decade at an annual rate of 20%. However, because of the novelty of these devices, there is limited literature on their learning curves and how they should be integrated into residency curricula. With the present review, the authors aimed to address these two points.

METHODS

A systematic review of the published English-language literature on PubMed, Ovid, Scopus, and Web of Science was conducted to identify studies describing the learning curve in spine robotics. Included articles described clinical results in patients using one of the following endpoints: operative time, screw placement time, fluoroscopy usage, and instrumentation accuracy. Systems examined included the Mazor series, the ExcelsiusGPS, and the TiRobot. Learning curves were reported in a qualitative synthesis, given as the mean improvement in the endpoint per case performed or screw placed where possible. All studies were level IV case series with a high risk of reporting bias.

RESULTS

Of 1579 unique articles, 97 underwent full-text review and 21 met the inclusion and exclusion criteria; 62 articles were excluded for not presenting primary data for one of the above-described endpoints. Of the 21 articles, 18 noted the presence of a learning curve in spine robots, which ranged from 3 to 30 cases or 15 to 62 screws. Only 12 articles performed regressions of one of the endpoints (most commonly operative time) as a function of screws placed or cases performed. Among these, increasing experience was associated with a 0.24- to 4.6-minute decrease in operative time per case performed. All but one series described the experience of attending surgeons, not residents.

CONCLUSIONS

Most studies of learning curves with spine robots have found them to be present, with the most common threshold being 20 to 30 cases performed. Unfortunately, all available evidence is level IV data, limited to case series. Given the ability of residency to allow trainees to safely perform these cases under the supervision of experienced senior surgeons, it is argued that a curriculum should be developed for senior-level residents specializing in spine comprising a minimum of 30 performed cases.

Initial Intraoperative Experience with Robotic-Assisted Pedicle Screw Placement with Cirq® Robotic Alignment: An Evaluation of the First 70 Screws

BACKGROUND

Robot-guided spine surgery is based on a preoperatively planned trajectory that is reproduced in the operating room by the robotic device. This study presents our initial experience with thoracolumbar pedicle screw placement using Brainlab's Cirq^® surgeon-controlled robotic arm (BrainLab, Munich, Germany).

METHODS

All patients who underwent robotic-assisted implantation of pedicle screws in the thoracolumbar spine were included in the study. Our workflow, consisting of preoperative imagining, screw planning, intraoperative imaging with automatic registration, fusion of the preoperative and intraoperative imaging with a review of the preplanned screw trajectories, robotic-assisted insertion of K-wires, followed by a fluoroscopy-assisted insertion of pedicle screws and control iCT scan, is described.

RESULTS

A total of 12 patients (5 male and 7 females, mean age 67.4 years) underwent 13 surgeries using the Cirq^® Robotic Alignment Module for thoracolumbar pedicle screw implantation. Spondylodiscitis, metastases, osteoporotic fracture, and spinal canal stenosis were detected. A total of 70 screws were implanted. The mean time per screw was 08:27 ± 06:54 min. The mean time per screw for the first 7 surgeries (first 36 screws) was 16:03 ± 09:32 min and for the latter 6 surgeries (34 screws) the mean time per screw was 04:35 ± 02:11 min (p < 0.05). Mean entry point deviation was 1.9 ± 1.23 mm, mean deviation from the tip of the screw was 2.61 ± 1.6 mm and mean angular deviation was 3.5° ± 2°. For screw-placement accuracy we used the CT-based Gertzbein and Robbins System (GRS). Of the total screws, 65 screws were GRS A screws (92.85%), one screw was a GRS B screw, and two further screws were grade C. Two screws were D screws (2.85%) and underwent intraoperative revision. There were no perioperative deficits.

CONCLUSION

Brainlab's Cirq^® Robotic Alignment surgeon-controlled robotic arm is a safe and beneficial method for accurate thoracolumbar pedicle screw placement with high accuracy.

Operative time and learning curve between fluoroscopy-based instrument tracking and robot-assisted instrumentation for patients undergoing minimally invasive transforaminal lumbar interbody fusion (MIS-TLIF)

INTRODUCTION

Instrument-navigation modalities including CT-guided and robot-assisted methods claim both efficacy and accuracy when applied to spine surgery, yet often increase setup and operating times which can translate to increased costs. To see the impact of different technologies on surgical efficiency, we studied the impact of a single surgeon's experience with a multitude of instrument navigational technologies.

METHODS

Consecutive patients undergoing minimally invasive transforaminal lumbar interbody fusion (MIS-TLIF) were analyzed. Consecutive cases were done with assistance of a robot (Mazor, Medtronic, Minneapolis, MN), with the assistance of fluoroscopic instrument-tracking (TrackX, North Carolina, USA), or fluoroscopy alone without adjunctive navigation in consecutive blocks of time. The cases done without assistance were used to normalize for number of interbody implants and decompressions performed as well as hardware removal if needed. Age, body mass index (BMI), sex, operative levels, laminectomy, need for hardware removal, and total operative time were recorded.

RESULTS

A total of 119 cases (74 conventional, 13 robot-assisted, 32 instrument-tracking) were included in analysis. There were no significant differences in age, sex, or BMI between modalities. Average total operative time for robot-assisted, and instrument-tracking-assisted cases was 175.46 ± 46.86 min 119.63 ± 34.33 min, respectively, for each level (p < 0.05 across each group). After normalization against operative times from similar cases performed with conventional fluoroscopy, robotic-navigation added an average of 42.25 ± 28.35 min while use of instrument-tracking saved an average of 13.88 ± 38.69 min. There was no learning curve seen using robotic navigation, as operative times remained consistently longer than similar cases using conventional fluoroscopy and showed no sign of improvement over time. Cases using instrument-tracking were initially slower but trended downwards through approximately 11 patients, at which point operative times were consistently quicker (R² = 0.39). None of the assisted cases were abandoned in favor of standard fluoroscopy or required hardware revision.

CONCLUSION

Enabling technology can have a significant impact on surgical efficiency. Compared to MIS-TLIFs performed with standard fluoroscopy, those done with robotic-assistance consistently negatively impacted operative times while instrument-tracking was associated with a short learning curve and in the majority of cases studied showed improved operative times.