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Since its first application in 2000 by Binder and Kramer , the uptake of robotic surgery has been rapid . This ever evolving surgical technique has cemented itself as the ‘gold standard’ operative procedure for the removal of the prostate gland. In 2007 it was estimated that 68% of radical prostatectomies in the USA were performed using robotic assistance . The potential benefits of robotic surgery are multiple including; shorter recovery time, less postoperative pain, lower blood loss and improved cosmesis. The most commonly used system is the da Vinci® Surgical System (dVSS) from Intuitive Surgical, Inc., and consists of two main components; master console and a slave robot [2, 4]. A surgeon provides input through manipulation of the master console which, in turn, controls a slave robot to perform the necessary motions on the patient.
Robotic training poses several unique challenges to educators, trainees and training programme directors alike. During conventional open and laparoscopic surgery the mentoring surgeon is adjacent to the trainee and has the same view of the procedure, as well as being able to take over at any given moment where patient safety may be compromised. This is currently not the case in robotic assisted procedures as only one surgeon can be at the operating console at any given time thus competency before embarking on robotic procedures is paramount. From the trainees' perspective, with limits in working hours, fear of litigation and financial constraints, the prospect of training in robotic surgery seems a daunting task given the individual nature of the surgery. Trainees and programme directors have recognised that ‘on the job’ training will be difficult in this context and are therefore turning to alternative methods to solve the robotic training conundrum, namely robotic fellowships and simulation training.
Surgical simulation has advanced tremendously over the last two decades with the development of laparoscopic and now robotic surgery. This novel approach to surgical training has been validated as a training and assessment tool and has been shown to improve a surgeon's performance in the operating room [5-7].
Surgical simulator training can be separated into two broad categories: physical (mechanical) simulators, in which the task is performed under videoscopic guidance within usually a box trainer and ‘virtual reality’ (VR) simulators, where the task is performed on a computer-based platform and artificially generated virtual environments. Improvements in computer processing have led to more realistic and sensitive VR simulators, which are now capable of providing statistical feedback on the surgeons performance, a quality that is not shared by mechanical or cadaveric simulator trainers.
Before a surgical simulator can be used to assess the competency of surgeons, the simulator must undergo initial testing across a variety of parameters. This would include the assessment of face validity, which examines the realism of the simulator; construct validity, is it able to differentiate novice from experienced operators; context validity, examines whether the device can teach what it is supposed to teach; concurrent validity, the extent to which the results of the test correlate with the ‘gold standard’ tests known to measure the same domain; and predictive validity, the extent to which an assessment will predict future performance [8-10] (Fig. 1). The validity of mechanical and VR simulators in the context of laparoscopic surgery has been established. However, their effectiveness in training surgeons on robotic surgical systems is less clear.
In this systematic review we identified available robotic surgery simulators, explored the evidence supporting the effectiveness of the various platforms in terms of feasibility, reliability, validity, acceptability, educational impact and cost-effectiveness. This article also highlights the deficiencies and future work required to advance robotic surgical training.
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- Materials and Methods
- Conflict of Interest
To our knowledge this is the first systematic review of all the simulation options available to robotic surgeons. They provide a safe environment for trainees to develop their skills. This generation of robotic simulators has provided more questions than answers. Firstly, there is a lack of standardisation upon which the metrics of simulator quality are tested in each of the different platforms. For example, there is no agreed definition upon which to assess face validity, whether it is the ‘very close/somewhat close’  scale devised for the RoSS platform or the visual analogue scale described for the dVSS . Similarly, there are no consistent definitions as to what qualifies as a ‘novice’ or an ‘expert’. These concerns apply to each of the metrics used to measure simulators, and until these fundamental issues are addressed, we will never be able to rigorously study and compare these simulators and provide evidence-based solutions on how best to train the current and future generations of robotic surgeons.
Unfortunately, most of the exercises currently available on VR simulators are generic tasks testing hand–eye co-ordination, tissue manipulation, dissection, suturing and knot tying. There is no evidence to suggest which exercises lead to improved real-setting performance. Training scenarios for specific procedures incorporating challenging scenarios and complications are under development and will be much welcomed .
Further questions remain about the use of simulation training in the context of different skill levels. It has been shown that simulation models are valid and reliable for the initial phase of training and assessment in urological procedures; however, this is not the case for advanced and specialist level skill learning . In a training report by Davis et al. , the trainers were successful in teaching the introductory steps to robotic prostatectomy but their exposure to advanced steps were more limited, and often incomplete. Consequently, we advocate the use of robotic simulation in the early phase of robotic training. Further studies investigating its effectiveness in more complex situations and skills levels are required.
We used the criteria proposed by van de Vlueten  and Ahmed et al.  to evaluate the quality of each study. All of the simulators except RoSS have demonstrated face, content and construct validity but the numbers in these studies remain small. Educational impact was shown in eight studies and in all commercially available simulators except SEP. Evidence of criterion validity, such as predictive or concurrent validity, was very sparse. Other parameters, such as inter-rater and inter-item reliability, feasibility, acceptability, and cost-effectiveness of the simulation platforms were not evaluated by any of the studies. Similarly no group has validated the use of animal models and freshly frozen cadavers, and structured skills training based on observation for robotic surgery.
Data analysis was conducted using classic test theory. There was a wide variability and inconsistency of statistical methods used to evaluate data.
Given the lack of comparative studies between the different simulators the current body of evidence does not identify any one simulator being more effective in training the next generation of robotic surgeons than another. Each platform has the capability to train and assess a range of different robotic skills fundamental to the technique (Table 2). Unlike the dVSS the MdVT and RoSS platforms feature user interfaces that are similar to but not exact duplicates of the dVSS console used in clinical practice. The ProMIS simulator enables virtual and physical reality to be used together and has been investigated in the laparoscopic setting previously [33, 34]. The randomised control trial by Feifer et al.  represents the highest level of evidence for any of the simulators currently available. Their study showed that the use of ProMIS and LapSim simulators in conjunction with each other could improve robotic console performance. Interestingly, the LapSim group showed no improvement, and it was therefore not clear what contribution LapSim had on the overall improvement seen when both simulators were used in conjunction. Despite SEPs level of validation its face validity must come into question given that the participants in the van der Meijden et al.  study commented so negatively on the hardware, coupled with the MIS experts being so highly critical of the overall ergonomics of the training apparatus. Its biggest disadvantage lies in the fact that the images are not three-dimensional (3D), a fundamental concept pertaining to robotic compared with laparoscopic surgery. Further studies or perhaps even hardware upgrades to convert the 2D simulator into a 3D platform are therefore warranted.
Table 2. Simulator properties
|Developer||Simulated surgical systems||Sim surgery||CAE healthcare||Mimic||Intuitive surgical|
|Camera and clutching||Yes||No||No||Yes||Yes|
|Fourth arm integration||Yes||No||No||Yes||Yes|
|Needle control and driving||Yes||No||Yes||Yes||Yes|
|Energy and dissection||Yes||No||No||Yes||Yes|
|Developed for robotic surgery||Yes||Yes||No||Yes||Yes|
|Cost, USA dollars||120 000||62 000||35 000||158 000||89 000|
More studies have been conducted using the MdVT platform than any other, with three out of the four showing face, content and construct validity [23-25]. The Intuitive simulator has the distinct advantage that the same company who has developed the dVSS manufactures it. However, only one study has validated its use as a training tool .
With current level of validation of the available robotic simulators can be integrated as an adjunct to the basic phase of robotic training (Fig. 3). Until further studies can evaluate these simulators in greater detail this integration is likely to be on a local level in centres with significant funding and research capabilities. The choice of simulator currently is also likely to be department specific. This will require committees of surgeons with special expertise to assess robotic competence. National implementation of robotic surgical simulation training requires directives from national organisations to ensure that a structured, standardised approach is used. It is essential that competence can be defined in accordance with proficiency levels and that validated assessment tools are developed . Formal assessments of robotic surgeons in training have been attempted but the evaluation tools used were subjective and had not been validated [31, 35]. Such evaluation tools need to be reproducible and objective to accurately examine a surgeon's technical and non-technical skills.
Figure 3. A suggested time frame for when simulation training can be implemented within a urology residency programme.
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Within the robotic operating theatre non-technical factors, e.g. communication, team working, decision making, and judgment are key domains that must also be honed to ensure one possesses the ability for independent and competent practice [36, 37]. As yet there are very few if any team-based robotic simulation environments that have been able to encompass these important non-technical domains.
Recent years have witnessed trainees, trainers and more experienced robotic surgeons alike embracing VR training for robotic surgery with great optimism  (Fig. 4) [18, 38-41]. However, the cost of the robotic system alone is in the order of several million dollars, therefore it is most cost effective to devote as much of the surgical robot's time to performance of actual procedures. Therefore, the availability of such expensive equipment for training is usually low. With Intuitive Surgical, Inc., developing new robotic simulators, such as the latest six-arm robot, an advance on the older three-arm device and further developments in the pipeline, institutions with dated systems can donate old systems to their robotic training programmes. Funding from universities, charities and registered health organisations can aid in the development of simulation-training programmes and in the acquisition of the simulators themselves. With the ever increasing market competition between the different simulator manufacturers the cost of the simulators may decrease in the near future.
To date there is only one randomised control trial investigating the simulators available, and this looked at educational impact alone . In order to justify the costs, VR simulators will require further validation studies with greater sample sizes. Despite these issues robotic surgical simulators hold the greatest potential for robotic surgical training in the 21st century.
This article has some limitations. First, we may have missed a few relevant studies. We reviewed various databases with free text and Medical Subject Headings (MeSH®) terms to overcome this. Second, we could not use formal meta-analytical methods to pool results, as the included studies used different measuring tools and outcome measures for all metrics of simulator quality. There are several components to the simulators that were not investigated, e.g. concurrent and predictive validity, inter-rater and inter-item reliability, feasibility, acceptability, and cost-effectiveness. However this reflects paucity in the available data of these factors in published studies.