Virtual reality simulations in robotic surgery training: a systematic review and meta-analysis

von Bechtolsheim F., Franz A., Schmidt S., Schneider A., La Rosée F., Radulova-Mauersberger O., Krause-Jüttler G., Hümpel A., Bodenstedt S., Speidel S., Weitz J., Distler M., Oehme F.

Abstract Background Virtual reality is a frequently chosen method for learning the basics of robotic surgery. However, it is unclear whether tissue handling is adequately trained in VR training compared to training on a real robotic system. Methods In this randomized controlled trial, participants were split into two groups for “Fundamentals of Robotic Surgery (FRS)” training on either a DaVinci VR simulator (VR group) or a DaVinci robotic system (Robot group). All participants completed four tasks on the DaVinci robotic system before training (Baseline test), after proficiency in three FRS tasks (Midterm test), and after proficiency in all FRS tasks (Final test). Primary endpoints were forces applied across tests. Results This trial included 87 robotic novices, of which 43 and 44 participants received FRS training in VR group and Robot group, respectively. The Baseline test showed no significant differences in force application between the groups indicating a sufficient randomization. In the Midterm and Final test, the force application was not different between groups. Both groups displayed sufficient learning curves with significant improvement of force application. However, the Robot group needed significantly less repetitions in the three FRS tasks Ring tower (Robot: 2.48 vs. VR: 5.45; p < 0.001), Knot Tying (Robot: 5.34 vs. VR: 8.13; p = 0.006), and Vessel Energy Dissection (Robot: 2 vs. VR: 2.38; p = 0.001) until reaching proficiency. Conclusion Robotic tissue handling skills improve significantly and comparably after both VR training and training on a real robotic system, but training on a VR simulator might be less efficient.

Chen C.C., Malpani A., Waldram M.M., Romanczyk C., Tanner E.J., Fader A.N., Scheib S.A., Hager G.D., Vedula S.S.

The objective was to study the effect of immediate pre-operative warm-up using virtual reality simulation on intraoperative robot-assisted laparoscopic hysterectomy (RALH) performance by gynecology trainees (residents and fellows). We randomized the first, non-emergent RALH of the day that involved trainees warming up or not warming up. For cases assigned to warm-up, trainees performed a set of exercises on the da Vinci Skills Simulator immediately before the procedure. The supervising attending surgeon, who was not informed whether or not the trainee was assigned to warm-up, assessed the trainee’s performance using the Objective Structured Assessment for Technical Skill (OSATS) and the Global Evaluative Assessment of Robotic Skills (GEARS) immediately after each surgery. We randomized 66 cases and analyzed 58 cases (30 warm-up, 28 no warm-up), which involved 21 trainees. Attending surgeons rated trainees similarly irrespective of warm-up randomization with mean (SD) OSATS composite scores of 22.6 (4.3; warm-up) vs 21.8 (3.4; no warm-up) and mean GEARS composite scores of 19.2 (3.8; warm-up) vs 18.8 (3.1; no warm-up). The difference in composite scores between warm-up and no warm-up was 0.34 (95% CI: −1.44, 2.13), and 0.34 (95% CI: −1.22, 1.90) for OSATS and GEARS respectively. Also, we did not observe any significant differences in each of the component/subscale scores within OSATS and GEARS between cases assigned to warm-up and no warm-up. Performing a brief virtual reality-based warm-up before RALH did not significantly improve the intraoperative performance of the trainees.

Shi J., Luo D., Wan X., Liu Y., Liu J., Bian Z., Tong T.

For clinical studies with continuous outcomes, when the data are potentially skewed, researchers may choose to report the whole or part of the five-number summary (the sample median, the first and third quartiles, and the minimum and maximum values) rather than the sample mean and standard deviation. In the recent literature, it is often suggested to transform the five-number summary back to the sample mean and standard deviation, which can be subsequently used in a meta-analysis. However, if a study contains skewed data, this transformation and hence the conclusions from the meta-analysis are unreliable. Therefore, we introduce a novel method for detecting the skewness of data using only the five-number summary and the sample size, and meanwhile, propose a new flow chart to handle the skewed studies in a different manner. We further show by simulations that our skewness tests are able to control the type I error rates and provide good statistical power, followed by a simulated meta-analysis and a real data example that illustrate the usefulness of our new method in meta-analysis and evidence-based medicine.

Colborn K., Brat G., Callcut R.

This Viewpoint discusses the opportunities and risks of using 3 main areas of artificial intelligence in surgery: computer vision, digital transformation at the point of care, and electronic health records data.

Takagi K., Hata N., Kimura J., Kikuchi S., Noma K., Yasui K., Fuji T., Yoshida R., Umeda Y., Yagi T., Fujiwara T.

AbstractThe use of virtual reality for simulations plays an important role in the initial training for robotic surgery. This randomized controlled trial aimed to investigate the impact of educational video on the performance of robotic simulation. Participants were randomized into the intervention (video) group that received an educational video and robotic simulation training or the control group that received only simulation training. The da Vinci® Skills Simulator was used for the basic course, including nine drills. The primary endpoint was the overall score of nine drills in cycles 1–10. Secondary endpoints included overall, efficiency, and penalty scores in each cycle, as well as the learning curves evaluated by the cumulative sum (CUSUM) analysis. Between September 2021 and May 2022, 20 participants were assigned to the video (n = 10) and control (n = 10) groups. The video group had significantly higher overall scores than the control group (90.8 vs. 72.4, P < 0.001). Significantly higher overall scores and lower penalty scores were confirmed, mainly in cycles 1–5. CUSUM analysis revealed a shorter learning curve in the video group. The present study demonstrated that educational video training can be effective in improving the performance of robotic simulation training and shortening the learning curve.

Burke J.R., Fleming C.A., King M., El-Sayed C., Bolton W.S., Munsch C., Harji D., Bach S.P., Collins J.W.

AbstractRobot-assisted surgery (RAS) continues to grow globally. Despite this, in the UK and Ireland, it is estimated that over 70% of surgical trainees across all specialities have no access to robot-assisted surgical training (RAST). This study aimed to provide educational stakeholders guidance on a pre-procedural core robotic surgery curriculum (PPCRC) from the perspective of the end user; the surgical trainee. The study was conducted in four Phases: P1: a steering group was formed to review current literature and summarise the evidence, P2: Pan-Specialty Trainee Panel Virtual Classroom Discussion, P3: Accelerated Delphi Process and P4: Formulation of Recommendations. Forty-three surgeons in training representing all surgical specialties and training levels contributed to the three round Delphi process. Additions to the second- and third-round surveys were formulated based on the answers and comments from previous rounds. Consensus opinion was defined as ≥ 80% agreement. There was 100% response from all three rounds. The resulting formulated guidance showed good internal consistency, with a Cronbach alpha of > 0.8. There was 97.7% agreement that a standardised PPCRC would be advantageous to training and that, independent of speciality, there should be a common approach (95.5% agreement). Consensus was reached in multiple areas: 1. Experience and Exposure, 2. Access and context, 3. Curriculum Components, 4 Target Groups and Delivery, 5. Objective Metrics, Benchmarking and Assessment. Using the Delphi methodology, we achieved multispecialty consensus among trainees to develop and reach content validation for the requirements and components of a PPCRC. This guidance will benefit from further validation following implementation.

Chahal B., Aydın A., Amin M.S., Ong K., Khan A., Khan M.S., Ahmed K., Dasgupta P.

Due to its advantages over open surgery and conventional laparoscopy, uptake of robot-assisted surgery has rapidly increased. It is important to know whether the existing open or laparoscopic skills of robotic novices shorten the robotic surgery learning curve, potentially reducing the amount of training required. This systematic review aims to assess psychomotor skill transfer to the robot in clinical and simulated settings. PubMed, EMBASE, Cochrane Library and Scopus databases were systematically searched in accordance with PRISMA guidelines from inception to August 2021 alongside website searching and citation chaining. Article screening, data extraction and quality assessment were undertaken by two independent reviewers. Outcomes included simulator performance metrics or in the case of clinical studies, peri- and post-operative metrics. Twenty-nine studies met the eligibility criteria. All studies were judged to be at high or moderate overall risk of bias. Results were narratively synthesised due to heterogeneity in study designs and outcome measures. Two of the three studies assessing open surgical skill transfer found evidence of successful skill transfer while nine of twenty-seven studies evaluating laparoscopic skill transfer found no evidence. Skill transfer from both modalities is most apparent when advanced robotic tasks are performed in the initial phase of the learning curve but quality and methodological limitations of the existing literature prevent definitive conclusions. The impact of incorporating laparoscopic simulation into robotic training curricula and on the cost effectiveness of training should be investigated.

Khan M.T., Patnaik R., Lee C.S., Willson C.M., Demario V.K., Krell R.W., Laverty R.B.

The use of robotic surgery has increased exponentially in the United States. Despite this uptick in popularity, no standardized training pathway exists for surgical residents or practicing surgeons trying to cross-train onto the platform. We set out to perform a systematic review of existing literature to better describe and analyze existing robotic surgical training curricula amongst academic surgery programs. A systematic electronic search of the PubMed, Cochrane, and EBSCO databases was performed for articles describing simulation in robotic surgery from January 2010 to May 2022. Medical Subject Heading (MeSH) terms and keywords used to conduct this search were “Robotic,” “Surgery,” “Robotic Surgery,” “Training,” “Curriculum,” “Education,” and “Residency Program.” A total of 110 articles were identified for the systematic review. After screening the titles and abstracts, a total of 36 full-text original articles were included in this systematic review. Of these, 24 involved robotic surgery curricula designed to teach general robotic skills, whereas the remaining 12 were for teaching procedure specific skills. Of the 24 studies involving general robotic skills, 13 included didactics as a part of the curriculum, 23 utilized virtual reality trainers, 3 used inanimate tissue, and 1 used live animal models. Of the 12 papers reviewed regarding procedure specific curricula, seven involved urologic procedures (radical prostatectomy and nephrectomy), two involved general surgical procedures (colectomy and Roux-en-Y gastric bypass surgery), two involved obstetrics and gynecology procedures (hysterectomy with myomectomy and sacrocolpopexy, hysterectomy with pelvic lymphadenectomy) and one involved a cardiothoracic surgery procedure (robotic internal thoracic artery harvest). With the rapid implementation of robotic surgery, training programs have been tasked with the responsibility of ensuring their trainees are adequately proficient in the platform prior to graduation. However, due to the lack of uniformity between surgical training programs, when it comes to robotic surgical experience, a strong need persists for a standardized national robotics training curriculum.

Seeliger B., Collins J.W., Porpiglia F., Marescaux J.

Surgical robotic systems are not only telemanipulation tools, but also computer interfaces integrating multiple additional features (e.g., real-time display of 3D reconstructions of preoperative imaging, allowing virtual and augmented reality guidance). Double console systems already support proctoring within the same operating room, and they could be set up at any distance with an adequate network connection, allowing for teleproctoring and intraoperative telesurgical assistance. In 2001, the Lindbergh Operation demonstrated that remote robotic surgery can be performed safely. This completely telesurgical transatlantic cholecystectomy was performed via the surgeon console in New York, United States, controlling the ZEUS robotic system’s patient-side cart in Strasbourg, France. Back in the early years of robotic surgery, the cost for the availability of a high-speed terrestrial optical-fiber network with asynchronous transfer mode (ATM) technology for data transport was substantial, and remote telesurgery was cost-effective neither for routine procedures nor for increasing access to healthcare. The advent of fifth-generation cellular networks (5G) generates a technology standard for broadband connectivity with low latency. Its availability provides a more economical solution than ATM technology, and 5G networks will become the backbone for a democratized robotic telesurgery. Download speeds can reach the gigabit per second (Gbit/s) range, and broadband capacity will further increase during the rollout of 5G networks. In combination with near-instantaneous latency, 5G will even allow the integration of virtual and augmented reality into telesurgery.

Nakamoto K., Jones D.B., Adra S.W.

Gamification applies game design elements to non-game contexts in order to engage participation and increase learner motivation. Robotic surgery is gaining popularity in general surgery but requires specialized technical skills. We sought to determine whether gamification of robotic simulation training could increase robotic simulator utilization among general surgery residents. General surgery residents were recruited and sent weekly progress on simulator performance including leaderboards for 4 weeks during the intervention periods. There were also two control periods setup in an ABAB study design. Usage time and mean scores were compared between the control periods and intervention periods. A post-study qualitative assessment interview using semi-structured interviews determined barriers and motivational components of simulator usage. Fifteen general surgery residents enrolled in the study (n = 15). Intervention increased total simulator usage time 9.7-fold from 153 to 1485 min. Total simulator days increased threefold from 9 to 27 days. Resident participation increased from 33 to 53%. Median average scores were higher during the intervention periods (58.8 and 81.9 vs 44.0). During the first intervention period, median individual-level simulator usage time increased 17 min (P = 0.03). However, there was no individual-level increase in median usage minutes or days during the second intervention period. Qualitative assessment determined barriers to be limited time due to clinical duties, and simulator availability while motivational factors included competitive factors such as leaderboards and gaming aspects. Potential improvements were increasing attending visibility of scores to increase recognition of progress by the residents and creating dedicated time for training. Gamification of robotic simulation training increased general surgery resident participation, usage time and scores. Impact was not durable. Instituting dedicated practice time and more attending engagement may increase trainee motivation and performance.

Lam K., Abràmoff M.D., Balibrea J.M., Bishop S.M., Brady R.R., Callcut R.A., Chand M., Collins J.W., Diener M.K., Eisenmann M., Fermont K., Neto M.G., Hager G.D., Hinchliffe R.J., Horgan A., et. al.

The use of digital technology is increasing rapidly across surgical specialities, yet there is no consensus for the term ‘digital surgery’. This is critical as digital health technologies present technical, governance, and legal challenges which are unique to the surgeon and surgical patient. We aim to define the term digital surgery and the ethical issues surrounding its clinical application, and to identify barriers and research goals for future practice. 38 international experts, across the fields of surgery, AI, industry, law, ethics and policy, participated in a four-round Delphi exercise. Issues were generated by an expert panel and public panel through a scoping questionnaire around key themes identified from the literature and voted upon in two subsequent questionnaire rounds. Consensus was defined if >70% of the panel deemed the statement important and <30% unimportant. A final online meeting was held to discuss consensus statements. The definition of digital surgery as the use of technology for the enhancement of preoperative planning, surgical performance, therapeutic support, or training, to improve outcomes and reduce harm achieved 100% consensus agreement. We highlight key ethical issues concerning data, privacy, confidentiality and public trust, consent, law, litigation and liability, and commercial partnerships within digital surgery and identify barriers and research goals for future practice. Developers and users of digital surgery must not only have an awareness of the ethical issues surrounding digital applications in healthcare, but also the ethical considerations unique to digital surgery. Future research into these issues must involve all digital surgery stakeholders including patients.

van der Leun J.A., Siem G., Meijer R.P., Brinkman W.M.

Background: Since the transition of surgical robot systems into the medical field, physicians have had to develop new dexterity skills. The ideal learning environment for robotic surgery has yet to be discovered. Virtual reality (VR) simulation is a possible safe and economic method. In VR simulator training human feedback is hardly used, and an alternative may be found in video revision. The purpose of this study is to investigate whether adding video review to VR simulation-based training in novice physicians improves their ability to complete a complex robot task. In addition, the secondary goal is to investigate whether the skills learned on the robotic simulator can be transferred to a real robotic system. Materials and Methods: Forty participants, medical students and, medical-PhD candidates, from one university hospital were included. Baseline dexterity skills were measured through completion of a vesicourethral anastomosis on a VR robot simulator and the da Vinci robot. Participants were randomized into a video and control group. The video group practiced skills on the robot simulator with intermediate video revision, whereas the control group had intermediate pause instead. Postintervention dexterity skills were measured using the same exercises as the baseline tests. Results: No significant differences were found in baseline performance. Postintervention results on the VR simulator show that the video group commits significantly fewer injuries to the urethra and sutures at a greater optimal depth. The control group was significantly faster, had less camera travel, and had their instruments less out of view. On the da Vinci robot, participants in both groups performed significantly faster and had better global evaluative assessment of robotic skill score after the training sessions on the VR simulator. Conclusions: Video revision significantly improves the quality of robotic skills in novice surgeons on the VR simulator, although at the expense of time. Furthermore, both groups demonstrated enhanced skills on the da Vinci robot after training sessions, which advocates transferability of skill.

Hardon S.F., Kooijmans A., Horeman R., van der Elst M., Bloemendaal A.L., Horeman T.

As global use of surgical robotic systems is steadily increasing, surgical simulation can be an excellent way for robotic surgeons to acquire and retain their skills in a safe environment. To address the need for training in less wealthy parts of the world, an affordable surgical robot simulator (PoLaRS) was designed. The aim of this pilot study is to compare learning curve data of the PoLaRS prototype with those of Intuitive Surgical’s da Vinci Skills Simulator (dVSS) and to establish face- and construct validity. Medical students were divided into two groups; the test group (n = 18) performing tasks on PoLaRS and dVSS, and the control group (n = 20) only performing tasks on the dVSS. The performance parameters were Time, Path length, and the number of collisions. Afterwards, the test group participants filled in a questionnaire regarding both systems. A total of 528 trials executed by 38 participants were measured and included for analyses. The test group significantly improved in Time, Path Length and Collisions during the PoLaRS test phase (P ≤ 0.028). No differences was found between the test group and the control group in the dVSS performances during the post-test phase. Learning curves showed similar shapes between both systems, and between both groups. Participants recognized the potential benefits of simulation training on the PoLaRS system. Robotic surgical skills improved during training with PoLaRS. This shows the potential of PoLaRS to become an affordable alternative to current surgical robot simulators. Validation with similar tasks and different expert levels is needed before implementing the training system into robotic training curricula.

Page M.J., McKenzie J.E., Bossuyt P.M., Boutron I., Hoffmann T.C., Mulrow C.D., Shamseer L., Tetzlaff J.M., Akl E.A., Brennan S.E., Chou R., Glanville J., Grimshaw J.M., Hróbjartsson A., Lalu M.M., et. al.

The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement, published in 2009, was designed to help systematic reviewers transparently report why the review was done, what the authors did, and what they found. Over the past decade, advances in systematic review methodology and terminology have necessitated an update to the guideline. The PRISMA 2020 statement replaces the 2009 statement and includes new reporting guidance that reflects advances in methods to identify, select, appraise, and synthesise studies. The structure and presentation of the items have been modified to facilitate implementation. In this article, we present the PRISMA 2020 27-item checklist, an expanded checklist that details reporting recommendations for each item, the PRISMA 2020 abstract checklist, and the revised flow diagrams for original and updated reviews.

Chen I.A., Ghazi A., Sridhar A., Stoyanov D., Slack M., Kelly J.D., Collins J.W.

Robot-assisted surgery is becoming increasingly adopted by multiple surgical specialties. There is evidence of inherent risks of utilising new technologies that are unfamiliar early in the learning curve. The development of standardised and validated training programmes is crucial to deliver safe introduction. In this review, we aim to evaluate the current evidence and opportunities to integrate novel technologies into modern digitalised robotic training curricula. A systematic literature review of the current evidence for novel technologies in surgical training was conducted online and relevant publications and information were identified. Evaluation was made on how these technologies could further enable digitalisation of training. Overall, the quality of available studies was found to be low with current available evidence consisting largely of expert opinion, consensus statements and small qualitative studies. The review identified that there are several novel technologies already being utilised in robotic surgery training. There is also a trend towards standardised validated robotic training curricula. Currently, the majority of the validated curricula do not incorporate novel technologies and training is delivered with more traditional methods that includes centralisation of training services with wet laboratories that have access to cadavers and dedicated training robots. Improvements to training standards and understanding performance data have good potential to significantly lower complications in patients. Digitalisation automates data collection and brings data together for analysis. Machine learning has potential to develop automated performance feedback for trainees. Digitalised training aims to build on the current gold standards and to further improve the ‘continuum of training’ by integrating PBP training, 3D-printed models, telementoring, telemetry and machine learning.