Robotic Surgical Assistants Research Articles

Does Robotic Surgical Assistant (ROSA) Functionally Aligned TKA Lead to Higher Satisfaction Than Conventional Mechanically Aligned TKA: A Propensity-Matched Pair Analysis

Does Robotic Surgical Assistant (ROSA) Functionally Aligned TKA Lead to Higher Satisfaction Than Conventional Mechanically Aligned TKA: A Propensity-Matched Pair Analysis

A novel robotic surgical assistant for total knee arthroplasty has a learning curve ranging from 6 to 14 cases and exhibits high accuracy in tibial bone cuts

BackgroundThe adoption of robot-assisted total knee arthroplasty (TKA) aims to enhance the precision of implant positioning and limb alignment. Despite its benefits, the adoption of such technology is often accompanied by an initial learning curve, which may result in increased operative times. This study sought to determine the learning curve for the ROSA (Robotic Surgical Assistant) Knee System (Zimmer Biomet) in performing TKA and to evaluate the accuracy of the system in executing bone cuts and angles as planned. The hypothesis of this study was that cumulative experience with this robotic system would lead to reduced operative times. Additionally, the ROSA system demonstrated reliability in terms of the accuracy and reproducibility of bone cuts.MethodsIn this retrospective observational study, we examined 110 medical records from 95 patients who underwent ROSA-assisted TKA performed by three surgeons. We employed the cumulative summation methodology to assess the learning curves related to operative time. Furthermore, we evaluated the accuracy of the ROSA Knee System in performing TKA by comparing planned versus validated values for femoral and tibial bone cuts and angles.ResultsThe learning curve for the ROSA Knee System spanned 14, 14, and 6 cases for the respective surgeons, with operative times decreasing by 22 min upon reaching proficiency (70.8 vs. 48.9 min; p < 0.001). Significant discrepancies were observed between the average planned and validated cuts and angles for femoral bone cuts (0.4 degree ± 2.4 for femoral flexion, 0.1 degree ± 0.6 for femoral coronal alignment, 0.3 mm ± 1.2 for distal medial femoral resection, 1.4 mm ± 8.8 for distal lateral femoral resection) and hip–knee–ankle axis alignment (0.3 degree ± 1.9 )(p < 0.05) but not for tibial bone cuts. Differences between planned and validated measurements during the learning and proficiency phases were nonsignificant across all parameters, except for the femoral flexion angle (0.42 degree ± 0.8 vs. 0.44 degree ± 2.7) (p = 0.49).ConclusionThe ROSA Knee System can be integrated into surgical workflows after a modest learning curve of 6 to 14 cases. The system demonstrated high accuracy and reproducibility, particularly for tibial bone cuts. Acknowledging the learning curve associated with new robot-assisted TKA technologies is vital for their effective implementation.

Analysis of the Initial Learning Curve for Robotic-Assisted Total Knee Arthroplasty Using the ROSA® Knee System.

Background/Objectives: Total knee arthroplasty (TKA) is a frequent procedure in orthopedic surgery. Advances in TKA include the development of robotic-assisted systems. Training in raTKA entails a learning curve to achieve proficiency comparable to conventional manual TKA (maTKA). Methods: We conducted a prospective study of the learning curve in raTKA using the Robotic Surgical Assistant (ROSA) Knee System. The study included 180 patients (90 raTKAs; 90 maTKAs) and three surgeons (one with >15 years of experience in maTKA). The cumulative sum control chart method (CUSUM) was used to define the transition from the learning phase to the mastered phase in raTKA. Results: The learning curves were 43 cases (experienced surgeons) and 61 cases (all surgeons). Mean operative times for both phases in raTKA were longer than in maTKA (p < 0.001). In raTKA, operative times in the learning phase were longer compared to those in the mastered phase (p < 0.001). Operative times in the learning and mastered phases for all surgeons in raTKA were significantly longer compared to those in maTKA (p < 0.001); however, operative times of the experienced surgeon in the mastered phase of raTKA and in maTKA showed no differences. Conclusions: The learning curve in raTKA is dependent upon the surgeon's previous experience in maTKA.

Robotic-Assisted Stereoelectroencephalography: A Systematic Review and Meta-Analysis of Safety, Outcomes, and Precision in Refractory Epilepsy Patients.

Robotic assistance in stereoelectroencephalography (SEEG) holds promising potential for enhancing accuracy, efficiency, and safety during electrode placement and surgical procedures. This systematic review and meta-analysis, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and International Prospective Register of Systematic Reviews (PROSPERO) registration, delves into the latest advancements and implications of robotic systems in SEEG, while meticulously evaluating outcomes and safety measures. Among 855 patients suffering from medication-refractory epilepsy who underwent SEEG in 29 studies, averaging 24.6 years in age, the most prevalent robots employed were robotic surgical assistant (ROSA) (450 patients), Neuromate (207), Sinovation (140), and ISys1 (58). A total of 8,184 electrodes were successfully implanted, with an average operative time of 157.2 minutes per procedure and 15.1 minutes per electrode, resulting in an overall mean operative time of 157.7 minutes across all studies. Notably, the mean target point error (TPE) stood at 2.13 mm, the mean entry point error (EPE) at 1.48 mm, and postoperative complications occurred in 7.69% of robotically assisted (RA) SEEG cases (60), with 85% of these complications being asymptomatic. This comprehensive analysis underscores the safety and efficacy of RA-SEEG in patients with medication-refractory epilepsy, characterized by low complication rates, reduced operative time, and precise electrode placement, supporting its widespread adoption in clinical practice, with no discernible differences noted among the various robotic systems.

The learning curve to ROSA: cases needed to match the surgery time between a robotic-assisted and a manual primary total knee arthroplasty

PurposeLimited published data regarding the ROSA (Robotic Surgical Assistant) learning curve exist. This study evaluated the number of cases needed for an expert orthopaedic surgeon to master the ROSA system and match the operative time of robotic (raTKAs) and manual primary total knee arthroplasties (mTKAs).MethodsThis retrospective comparative cohort study included two hundred patients with primary knee osteoarthritis. The study group consisted of an expert surgeon’s first 100 raTKAs. The control group included 100 patients that underwent mTKAs from the same surgeon during the same period. The consecutive cases in each group were divided into ten subgroups, each of 10 cases. The groups were comparable concerning age, sex, BMI and Kellgren–Lawrence classification. We compared each subgroup's operative time and complications in mTKA and raTKA groups. We performed a cumsum analysis to construct the ROSA learning curve.ResultsThe first non-significant difference between the mTKAs and raTKAs operative times was observed in the subgroup of 62 to 71 cases. Till then, the operative time has been significantly lower for the mTKA than the raTKA group. The following groups of tens analysis (8th, 9th and 10th) showed no operative time difference between groups. The learning curve analysis demonstrated that the surgeon switched to the mastering phase from case 73 onwards. The two groups had no complication rate differences.ConclusionOur study demonstrated that about 70 cases are necessary for a senior surgeon to balance operative time between mTKAs and raTKAs using the ROSA system.

INTRODUCTION OF ROSA ROBOTIC ARM SYSTEM FOR TOTAL KNEE ARTHROPLASTY IS ASSOCIATED WITH A MINIMAL LEARNING CURVE FOR OPERATIVE TIME

The introduction of robotics for total knee arthroplasty (TKA) into the operating theatre is often associated with a learning curve and is potentially associated with additional complications. The purpose of this study was to determine the learning curve of robotic-assisted (RA) TKA within a multi-surgeon team.This prospective cohort study included 83 consecutive conventional jig-based TKAs compared with 53 RA TKAs using the Robotic Surgical Assistant (ROSA) system (Zimmer Biomet, Warsaw, Indiana, USA) for knee osteoarthritis performed by three high-volume (&gt; 100 TKA per year) orthopaedic surgeons. Baseline characteristics including age, BMI, sex and pre-operative Kellgren-Lawrence grade were well-matched between the conventional and RA TKA groups. Cumulative summation (CUSUM) analysis was used to assess learning curves for operative times for each surgeon. Peri-operative and delayed complications were reviewed.The CUSUM analysis for operative time demonstrated an inflexion point after 5, 6 and 15 cases for each of the three surgeons, or 8.7 cases on average. There were no significant differences (p = 0.53) in operative times between the RA TKA learning (before inflexion point) and proficiency (after inflexion point) phases. Similarly, the operative times of the RA TKA group did not differ significantly (p = 0.92) from the conventional TKA group. There was no discernible learning curve for the accuracy of component planning using the RA TKA system. The average length of post-operative follow-up was 21.3 ± 9.0 months. There was no significant difference (p &gt; 0.99) in post-operative complication rates between the groups.The introduction of the RA TKA system was associated with a learning curve for operative time of 8.7 cases. Operative times between the RA TKA and conventional TKA group were similar. The short learning curve implies this RA TKA system can be adopted relatively quickly into a surgical team with minimal risks to patients.

8890 Robotic Hysterectomies with Mini-Laparotomies for Enlarged Uteri

<h3>Study Objective</h3> Robotic surgical system assistance in hysterectomies has gained significant popularity amongst gynecologists for its ease of employment, improved visualization and minimally invasive approach in attempts to decrease intra-/post-operative complications and improve clinical outcomes. Assessment of clinical outcomes, specifically length of hospitalization and postoperative complication rates, of patients undergoing robotic hysterectomy with the addition of mini-laparotomy and in bag manual morcellation for extraction of enlarged uteri specimen was analyzed. <h3>Design</h3> Retrospective case series analysis of patients who received the treatment in question pooled from a private practice Obstetrics and Gynecology group. <h3>Setting</h3> N/A. <h3>Patients or Participants</h3> Surgical database of this private practice was reviewed and analyzed between 2015 and 2021 for patients undergoing this procedure for benign indications with enlarged uteri (namely uterine fibroids) yielding 52 eligible participants. <h3>Interventions</h3> After completion of a robotic-assisted laparoscopic hysterectomy of an enlarged uterus using the standard technique, the resulting specimen which could not easily be delivered vaginally, was placed into a surgical bag, brought through a mini-laparotomy (≤5cm) and manually morcellated to facilitate specimen extraction. <h3>Measurements and Main Results</h3> A total of 52 patients were included in the retrospective analysis revealing a 90.4% (47/52) same day discharges with 5 post-operative admissions; one for uncontrolled blood glucose and four for post-operative pain. The minimum uterine size requiring mini-laparotomy for extraction was found to be 12cm with at least a 366g weight. The average uterine size and weight requiring said intervention were found to be 18cm and 853g, respectively. Complications were also found to be rare with seven total reports of post-operative fever of which 5 pelvic collections were found and three microbiologically confirmed as pelvic abscesses necessitating drainage and parenteral antibiotics. <h3>Conclusion</h3> Mini-laparotomy within bag manual morcellation for enlarged uteri is a beneficial adjunct technique to robotic-assisted laparoscopic hysterectomies allowing the preservation of the benefits of minimally invasive surgery while also maintaining low complication rates.

Introduction of ROSA robotic-arm system for total knee arthroplasty is associated with a minimal learning curve for operative time

PurposeThe introduction of robotics for total knee arthroplasty (TKA) into the operating theatre is often associated with a learning curve and is potentially associated with additional complications. The purpose of this study was to determine the learning curve of robotic-assisted (RA) TKA within a multi-surgeon team.MethodsThis prospective cohort study included 83 consecutive conventional jig-based TKAs compared with 53 RA TKAs using the Robotic Surgical Assistant (ROSA) system (Zimmer Biomet, Warsaw, Indiana, USA) for knee osteoarthritis performed by three high-volume (> 100 TKA per year) orthopaedic surgeons. Baseline characteristics including age, BMI, sex and pre-operative Kellgren-Lawrence graded and Hip-Knee-Ankle Axis were well-matched between the conventional and RA TKA groups. Cumulative summation (CUSUM) analysis was used to assess learning curves for operative times for each surgeon. Peri-operative and delayed complications (infection, periprosthetic fracture, thromboembolism, and compromised wound healing) and revisions were reviewed.ResultsThe CUSUM analysis for operative time demonstrated an inflexion point after 5, 6 and 15 cases for each of the three surgeons, or 8.7 cases on average. There were no significant differences (p = 0.53) in operative times between the RA TKA learning (before inflexion point) and proficiency (after inflexion point) phases. Similarly, the operative times of the RA TKA group did not differ significantly (p = 0.92) from the conventional TKA group. There was no discernible learning curve for the accuracy of component planning using the RA TKA system. The average length of post-operative follow-up was 21.3 ± 9.0 months. There was one revision for instability in the conventional TKA group and none in the RA TKA group. There were no significant difference (p > 0.99) in post-operative complication rates between the conventional TKA and RA TKA groups.ConclusionsThe introduction of the RA TKA system was associated with a learning curve for operative time of 8.7 cases. Operative times between the RA TKA and conventional TKA group were similar. The short learning curve implies this RA TKA system can be adopted relatively quickly into a surgical team with minimal risks to patients.

Unsatisfactory accuracy of recent robotic assisting system ROSA for total knee arthroplasty

PurposeThe purpose of this study was to quantify accuracy of a recently FDA-approved robotic-assisted device.MethodsThirty-seven patients underwent TKA with the Robotic Surgical Assistant (ROSA) by the same operating surgeon and team over the course of 3 months. Intra-operative mechanical axis measurements, composed of alpha (α), beta (β), gamma (γ), and delta (δ) angles, and the hip-knee-ankle angle (HKA) were calculated by the ROSA. Post-operative mechanical implant angles were taken from 36″ stitched post-op films and measured in the PACS imaging system. Accuracy was assessed by comparing the percentage of postoperative long length films within 2° and 3° of the ROSA intra-operative plan.ResultsThe ROSA system accurately calculated the HKA, α, and β angles (95% CI), but was inaccurate in calculating both γ and δ angles. Using a window of ± 3° accuracy, the HKA, α and β angles were accurate at levels of 89, 100 and 92% respectively. In contrast, the sagittal relationships were considerably less accurate at 77 and 74% for the γ and δ angles respectively. Subsequently, the proportion of cases within 2 and 3 degrees of the intra-operative plan for resection angles was considered accurate for HKA (73% within 2°, 89% within 3°), α (92% within 2°, 100% within 3°), and β (76% within 2°, 92% within 3°) angles, but considered inaccurate for γ (51% within 2°, 77% within 3°) and δ angles (57% within 2°, 74% within 3°).ConclusionsThis study demonstrated that while the ROSA system seems to accurately predict coronal plane resections in TKA, it falls short in the sagittal plane. Further research in these deficiencies can provide insight into the overall efficacy of robotic assisted surgery in TKA.Level of EvidenceLevel III Therapeutic Study.

Total Knee Arthroplasty With Robotic Surgical Assistance Results in Less Physician Stress and Strain Than Conventional Methods.

Poor surgical ergonomics and physiological stress have been shown to impair surgical performance and cause injuries. The prevalence of musculoskeletal pain among arthroplasty surgeons is inordinately high. This study compared surgeon stress and strain during robotic-assisted total knee arthroplasty (rTKA) and conventional TKA (cTKA). Continuous cardiorespiratory and ergonomic data of a single surgeon were measured during 40 consecutive unilateral TKAs (20 rTKAs, 20 cTKAs) using a smart garment and wearable sensors. Heart rate (HR), HR variability, respiratory rate, minute ventilation, and calorie expenditure were used as surrogate measures for physiological stress. Intraoperative ergonomics were assessed by measuring cervical and lumbar flexion, extension and rotation, and shoulder abduction/adduction. Mean operative time was longer for rTKA (48.2 ± 9 vs 31.8 ± 7min, P < .001). Calories expended per minute was lower for rTKA (2.53 vs 3.50, P < .001). Total calorie expenditure in rTKA cases 11-20 was significantly lower than the first 10 (107.1 ± 27 vs 137.6 ± 24, P = .015), and lower than cTKA (112.3 ± 37). Mean HR was lower for rTKA (81.5 ± 4 vs 90.1 ± 5, P < .001). Minute ventilation was also lower for rTKA (14.9 ± 1 vs 17.0 ± 1.0 L/min, P < .001). Mean lumbar flexion as well as the percentage of time spent in a demanding flexion position >20° were significantly lower during rTKA (P < .001). rTKA resulted in less surgeon physiologic stress, energy expenditure per minute, and postural strain compared to cTKA. Robotic assistance may help to increase surgical efficiency and reduce physician workload, but further studies are needed to determine whether these benefits will reduce musculoskeletal pain and injury among surgeons.

277 Implementation of the ROSA Robot in a DGH

Abstract Introduction The ROSA (Robotic Surgical Assistant) is designed to assist orthopaedic surgeons performing total knee arthroplasty (TKA). It is postulated that the real-time data provided by the ROSA robot aids the surgeon by providing objective values to bone resections and soft tissue balance, enabling better implant selection and positioning. This is thought to improve patient outcomes through reduced post-operative pain, reduced hospital stays and reduced revision rate. To date, the ROSA robot has solely been implemented in tertiary centres in the UK. This study investigates the implementation of the ROSA robot in a district general hospital (DGH) in a geographically isolated region of the UK. Method The ROSA robot was installed in St. Michael’s Hospital, Cornwall in July 2021. Prior to its clinical deployment, six surgeons undertook saw bone training. Following this, six ROSA-assisted TKAs were performed in six weeks between July and August 2021. Logistical challenges to implementation included difficulty in ROSA component delivery given geographical isolation, consultant training time dedicated to ROSA operation in the context of a busy DGH trauma rota and increased operative time during the learning curve. Conclusions Despite the challenges faced, we have demonstrated that ROSA can be implemented in a rural, geographically distanced DGH. Given the use of robotic assistance in other surgical specialties, this is an exciting prospect for elective knee surgery in the future.

Concepts and techniques of a new robotically assisted technique for total knee arthroplasty: the ROSA knee system.

The ROSA (Robotic Surgical Assistant) Knee system (Zimmer Biomet, Warsaw, IN) for total knee arthroplasty (TKA) can be considered as collaborative robotics, where the surgeon remains in charge of the procedure and collaborates with a smart robotic tool, to perform the surgery with a high accuracy and reproducibility. The aim was to describe: (1) its concept and surgical technique; (2) its advantages and potential limits; (3) the early experience with this system. The goal during its development phase was to keep the surgeon active and at the center of the operation: the surgeon handles the saw and performs the cuts while the robotic arm places and holds the guide at the right place. The ROSA knee platform assists the surgeon for the distal femoral cut, the femoral component sizing and positioning, the tibial cut and the ligament balance. This robotic system has two options: image-based with 3D virtual model; or image-less, based on intraoperative landmarks acquisition. All the classic surgical techniques can be used: measured resection, gap balancing, functional alignment, kinematic alignment. Some techniques recently developed are more ROSA-specific: Robotic personalized TKA, ROSA-FuZion technique. Its advantages as compared to other available systems include: radiographs in standing position, collaborative robotic system where the robot completes the surgeon skills, "off-the-shelf" implants, predictive robotic with concept of machine learning incorporated into the system. Two cadaveric studies have reported the high accuracy and reproducibility of this device. This robotic system is recent and currently no clinical series has enough follow-up to report clinical outcomes. The ROSA knee system is a robotically assisted semi-autonomous surgical system with some specific characteristics. The aim of this collaborative robotic system is to improve the accuracy and reliability of the bone resections and the ligament balancing, without replacing the steps well performed by the surgeon.

Improved accuracy and reproducibility of a novel CT-free robotic surgical assistant for medial unicompartmental knee arthroplasty compared to conventional instrumentation: a cadaveric study.

Alignment errors in medial unicompartmental knee arthroplasty (UKA) predispose to premature implant loosening and polyethylene wear. The purpose of this study was to determine whether a novel CT-free robotic surgical assistant improves the accuracy and reproducibility of bone resections in UKA compared to conventional manual instrumentation. Sixty matched cadaveric limbs received medial UKA with either the ROSA® Partial Knee System or conventional instrumentation. Fifteen board-certified orthopaedic surgeons with no prior experience with this robotic application performed the procedures with the same implant system. Bone resection angles in the coronal, sagittal and transverse planes were determined using optical navigation while resection depth was obtained using calliper measurements. Group comparison was performed using Student's t test (mean absolute error), F test (variance) and Fisher's exact test (% within a value), with significance at p < 0.05. Compared to conventional instrumentation, the accuracy of bone resections with CT-free robotic assistance was significantly improved for all bone resection parameters (p < 0.05), other than distal femoral resection depth, which did not differ significantly. Moreover, the variance was significantly lower (i.e. fewer chances of outliers) for five of seven parameters in the robotic group (p < 0.05). All values in the robotic group had a higher percentage of cases within 2° and 3° of the intraoperative plan. No re-cuts of the proximal tibia were required in the robotic group compared with 40% of cases in the conventional group. The ROSA® Partial Knee System was significantly more accurate, with fewer outliers, compared to conventional instrumentation. The data reported in our current study are comparable to other semiautonomous robotic devices and support the use of this robotic technology for medial UKA. Cadaveric study, Level V.

Adoption of Robotic Arm-Assisted Total Hip Arthroplasty Results in Reliable Clinical and Radiographic Outcomes at Minimum Two-Year Follow Up

Introduction: Longevity and success of total hip arthroplasty (THA) is largely dependent on component positioning. While use of robotic platforms can improve this positioning, published evidence on its clinical benefits is limited. Therefore, the aim of this study was to assess the clinical outcomes of THA with robotic surgical assistance. Materials and Methods: We conducted an analysis of robotic arm-assisted primary THAs performed by a single surgeon utilizing a posterior approach. A total of 99 patients (107 cases) who had a minimum two-year follow up were identified. Their mean age was 61 years (range, 33 to 84 years), and their mean body mass index was 30.5 kg/m2 (range, 18.5 to 49.1 kg/m2). There were 56% female patients and primary osteoarthritis was the principal hip diagnosis in 88.8%. Operative times, lengths of hospital stay, and discharge dispositions were recorded, along with any complications. Modified Harris Hip Scores (HHS) were calculated to quantify clinical outcomes. Results: Mean postoperative increases in HHS at 2- to 5.7-year follow up was 33 points (range, 6 to 77 points). There were no complications attributable to the use of robotic assistance. Surgical-site complications were rare; one case underwent a revision for prosthetic joint infection (0.93%) but there were no dislocations, periprosthetic fractures, or cases of mechanical implant loosening. There was no evidence of progressive radiolucencies or radiographic failure. Discussion: Robotic arm-assisted THA resulted in low complication rates at minimum two-year follow up, with clinical outcomes comparable to those reported with manual surgery.1–4 The haptically-guided acetabular bone preparation enabled reliable cementless acetabular fixation and there were no adverse events related to the use of the robot. Dislocations were avoided in this case series. Randomized controlled clinical trials are needed to compare manual to robotic surgery and to investigate whether the precision found with this functional planning will reliably reduce the incidence of dislocations.

Collaborative Human–Robot Interaction Interface: Development for a Spinal Surgery Robotic Assistant

The growing introduction of robotics in non-industrial applications where the environment is unstructured and changing, has led to the need of development of safer and more intuitive, human–robot interfaces. In such environments, the use of collaborative robots has potential benefits, due to the combination of user experience, knowledge and flexibility with the robot’s accuracy, stiffness and repeatability. Nevertheless, in order to guarantee a functional collaboration in these environments, the interaction between user and robot must be intuitive, natural, fast and easy to use. On one hand, commercial collaborative robots are less accurate and less stiff than the traditional industrial ones, on the other hand, the later have not intuitive interaction interfaces. There are tasks in which the stiffness of industrial robots and the intuitive interaction interfaces of collaborative commercial robots, are desirable. This is the case of some robotic assisted surgical procedures, such as robotic assisted spine surgery, with high accuracy demands and with the need of intuitive surgeon–robot interaction. This paper presents a hand guiding methodology for functional human–robot collaboration and the introduction of novel algorithms to enhance its behavior. Also its implementation on a robotic surgical assistant for spine procedures is presented. It is emphasized how a traditional industrial robot can be used as a collaborative one when the available commercial collaborative robots do not have the required accuracy and stiffness for the task.

Efficiently Calibrating Cable-Driven Surgical Robots With RGBD Fiducial Sensing and Recurrent Neural Networks

Automation of surgical subtasks using cable-driven robotic surgical assistants (RSAs) such as Intuitive Surgical's da Vinci Research Kit (dVRK) is challenging due to imprecision in control from cable-related effects such as cable stretching and hysteresis. We propose a novel approach to efficiently calibrate such robots by placing a 3D printed fiducial coordinate frames on the arm and end-effector that is tracked using RGBD sensing. To measure the coupling and history-dependent effects between joints, we analyze data from sampled trajectories and consider 13 approaches to modeling. These models include linear regression and LSTM recurrent neural networks, each with varying temporal window length to provide compensatory feedback. With the proposed method, data collection of 1800 samples takes 31 minutes and model training takes under 1 minute. Results on a test set of reference trajectories suggest that the trained model can reduce the mean tracking error of the physical robot from 2.96 mm to 0.65 mm. Results on the execution of open-loop trajectories of the FLS peg transfer surgeon training task suggest that the best model increases success rate from 39.4 % to 96.7 %, producing performance comparable to that of an expert surgical resident. Supplementary materials, including code and 3D-printable models, are available at https://sites.google.com/berkeley.edu/surgical-calibration

Robotic Surgical Assistant Rehearsal: Combining 3-Dimensional-Printing Technology With Preoperative Stereotactic Planning for Placement of Stereoencephalography Electrodes.

The use of frameless stereotactic robotic technology has rapidly expanded since the Food and Drug Administration's approval of the Robotic Surgical Assistant (ROSA) in 2012. Although the use of the ROSA robot has greatly augmented stereotactic placement of intracerebral stereoelectroencephalography (sEEG) for the purposes of epileptogenic focus identification, the preoperative planning stages remain limited to computer software. To describe the use of a 3-dimensionally (3D)-printed patient model in the preoperative planning of ROSA-assisted depth electrode placement for epilepsy monitoring in a pediatric patient. An anatomically accurate 3D model was created and registered in a preoperative rehearsal session using the ROSA platform. After standard software-based electrode trajectory planning, sEEG electrodes were sequentially placed in the 3D model. Utilization of the 3D-printed model enabled workflow optimization and increased staff familiarity with the logistics of the robotic technology as it relates to depth electrode placement. The rehearsal maneuvers enabled optimization of patient head positioning as well as identification of physical conflicts between 2 electrodes. This permitted revision of trajectory planning in anticipation of the actual case, thereby improving patient safety and decreasing operative time. Use of a 3D-printed patient model enhanced presurgical positioning and trajectory planning in the placement of stereotactic sEEG electrodes for epilepsy monitoring in a pediatric patient. The ROSA rehearsal decreased operative time and increased efficiency of electrode placement.

Deep infiltrating endometriosis: Interest of the robotic approach for a fledgling team

Introduction: Mini-invasive surgery of deep endometriosis is challenging. Surgical difficulties related to the technical limitations of classical laparoscopic approach might be overcome with the use of robotic assistance. The aim of this study was to evaluate the safety and feasibility of robotic surgery for deep infiltrating endometriosis in the learning phase of our team. Methods: The 20 first cases of robotic-assisted laparoscopies for endometriosis were included over a 2-year period. Baseline characteristics of patients and surgical data were reviewed. Surgical outcomes and follow-up information of the patients were analyzed. Results: Twenty women were included. The mean age was 31.9 years (range: 25–44) and mean body mass index was 23 kg/m2 (range: 16–35). Ten patients had rectovaginal or uterosacral location only (50%) and nine women had deep infiltrating endometriosis with digestive or urinary tract lesions (45%). In addition to the gynecologic surgeon, urologic or visceral surgeons were required in 10 cases, and there were 3 cases where the three specialties were needed. The mean operative time was 183.9 min (range: 85–398) and no difference was observed between the first five cases and the last five cases. There was one laparoconversion, and only two urologic postoperative complications occurred. Conclusion: Thanks to the use of robotic surgical assistance and a multidisciplinary approach, and despite the start of the team for deep endometriosis care, no learning curve effect was observed regarding surgical procedures’ success, safety, or duration. The use of robotic assistance might improve the quality of care for women facing deep endometriosis.

Robot-guided stereoelectroencephalography without a computed tomography scan for referencing: Analysis of accuracy.

Recent studies with robot-guided stereotaxy use computed tomography (CT) scans for referencing. We will provide evidence that using preoperative MRI datasets referenced with a laser scan of the patient's face is sufficient for sEEG implantation. In total, 40 sEEG electrodes were implanted in five patients by the robotic surgical assistant (ROSA). The postoperative CT scan for identifying electrode positions was fused with the preoperative MRI-based planning data. The accuracy was determined by the target point error (TPE) and the entry point error (EPE), applying the Euclidean distance. The mean TPE amounted to 2.96mm, the mean EPE to 2.53mm. The accuracy was improved in 1.5T MRI: the mean TPE amounted to 1.72mm, the EPE to 0.97mm. No complications, haemorrhages, infections, etc., were observed. Robot-guided sEEG based on 3T MRI reduces radiation exposure for the patient and can still be performed safely.

The robotically-assisted oropharyngeal resection

The robotic technologies every day exert an increasingly greater influence on the surgical technique, with the well documented clinical applicability in reconstructive plastic surgery and oncology. At the Department of plastic surgery of I.M. Sechenov First Moscow state medical university, we have gained an extensive experience with the surgical treatment of the patients requiring the major oral robotic intervention. A novel application has recently been documented for the purpose of robotic surgical assistance to the patients presenting with oncological pathology including cancer of the oropharyngeal wall at the early stages of development (T1N0M0). The objective of the present study was to develop and introduce into the clinical practice of the robot-assisted methods for the treatment of malignant neoplasms of the head and neck organs. A hybrid technique was applied, i.e. the combination of the robot-assisted trans-oral surgical intervention and the microsurgical radial flap mobilization to correct the postoperative defect following the extraction of the tumour. The combination of these methods provided the excellent clinical results and positive dynamics of the patients' condition, with the swell apparent improvement of the quality of life. The early patient activation was due to the application of the minimally invasive transoral surgical technique, with the swallowing ability being reinstated within 14 days after the intervention by virtue of the proper flap function. All the patients successfully underwent the robot-assisted surgical manipulations without complications. The shorter rehabilitation time during the postoperative period in these patients made it possible to quicker initiate chemotherapy and radiotherapy. This significantly improved the patient's quality of life and prognosis due to the combined approach to the treatment of head and neck oncology. It is concluded that the incorporation of the robotic assistance into the program of surgical treatment significantly increases the effectiveness of the treatment and makes possible the combined approach to the treatment of oncological patients with manageable tumours of the head and neck region.