This website is intended for healthcare professionals only.

Newsletter      
Hospital Healthcare Europe
HOPE LOGO
Hospital Healthcare Europe

Share this article

Follow by Email
Facebook
Twitter

Virtual reality training for robotic surgery

Henk WR Schreuder MD PhD
18 May, 2016  

With the exponential growth of laparoscopic robotic surgery, the need for competence-based training curricula for residents, fellows and surgeons is increasing rapidly

Henk WR Schreuder MD PhD
Division of Women and Baby,
Department of Reproductive Medicine and Gynaecology,
University Medical Centre Utrecht,The Netherlands
Email: H.W.R.Schreuder@umcutrecht.nl
 
The introduction of robot-assisted laparoscopic surgery has revolutionised the field of minimal invasive surgery. This new technique is growing fast and is nowadays used within most of the surgical specialties.1
 
The rapid introduction of robotic procedures necessitates new training methods. The robotic system seems ideal for integrating various forms of simulation and virtual reality simulation in particular. While using virtual reality (VR), surgeons can develop their skills and pass their basic learning curve on a simulator before operating on live patients. Implementing VR training has the potential to create high quality, competence-based robotic training programmes, which could shortening the learning curve and thereby ensure patient safety and a positive surgical outcome. Simulation training could in this way also reduce the costs associated with overcoming the learning curve of the surgeon.
 
Robot-assisted laparoscopic surgery
Currently, the da Vinci® Surgical System (Intuitive Surgery, Mountain View, CA, USA) is the only commercially available telerobotic system for laparoscopic surgery. This system consists of three major components: the surgical console; the Insite Vision System®; and the patient side cart with the robotic arms. The surgical console can be placed anywhere in, or even outside, the operating room. While operating, the surgeon is viewing a stereoscopic image projected in the console and controls the robotic arms with hand manipulators and foot pedals. The position provides an optimal hand–eye alignment. 
 
The system provides no haptic (force) feedback, so surgeons must rely on visual feedback. In the Insite Vision System®, a three-dimensional high-definition view is created and the viewer provides six-to-ten times magnification of the operating field. Because of the three-dimensional view the visual feedback is excellent, and allows the surgeon to work very precisely. The robotic system has four robotic arms; the EndoWrist® instruments are attached to the arms. These instruments are one of the key components of the system. The wrist has a total of seven degrees of freedom, similar to the human hand, and the surgeon’s hand (fingertip) movements are translated to corresponding movements of the instruments by the computer. 
 
Very precise movements of the robotic instruments are possible because the computer filters out normal physiological hand tremor, avoids the reverse-fulcrum effect, which occurs in traditional laparoscopy, and has the opportunity for motion scaling. In this way, a number of the disadvantages of conventional laparoscopy are eliminated, which results generally in a shorter learning curve for robot-assisted laparoscopy than for conventional laparoscopy. Alongside the surgical advantages are the ergonomic advantages for the surgeon, which reduce the physical complaints associated with conventional laparoscopic surgery.2
 
Currently, the main disadvantage of robotic-assisted laparoscopic surgery is the high cost. The cost is mainly reflected in the purchase and maintenance of the robotic system, together with the cost of the learning curve. The cost of the initial learning curve for the surgeon is high and can vary widely, depending on the surgeon, the operating team and the case volume.3 To overcome these high costs, the concept of good training programmes in high-volume centres are of great importance. 
 
In such centres, the learning curve can be rapidly traversed and costs minimised. When leaving out the initial capital investment for the robotic system and extra costs during the learning curve, there is not a great cost difference between conventional laparoscopy or open surgery. 
 
Training robotic surgery
With the increase in robot-assisted procedures, there is a concomitant rising demand for training methods for the da Vinci® Surgical System. Today, the number of surgeons who need to be trained in robotic surgery is growing rapidly, and in the near future, robotic training probably needs to be implemented in the formal medical training for urology, gynaecology and other specialties. 
 
When constructing a training programme for robotic surgery, several training modalities can be used. In general, a programme starts with knowledge development, followed by skills training using a combination of simulation modalities (dry lab, virtual reality, animal models or human cadavers), and is followed by real-life case observation in the operating room. When starting with actual robotic surgery there is a role for bedside assisting, mentoring and proctoring.4 Knowledge about the robotic system and the technology can be gained at a wide variety of courses worldwide. An e-learning model is available to ensure that surgeon starting with robotic surgery has the same basic level of theoretical knowledge. 
 
Just like in conventional laparoscopy, dry-lab training for robotic surgery can be carried out in a skills laboratory. In such facilities, exercises on pelvic trainers and other exercises can be performed. The main disadvantage of dry-lab training is the lack of objective assessment and the substantial costs associated with an extra robotic system in the training facility. However, a skills laboratory usually has the advantage of higher accessibility. With this in mind, most hospitals could probably not afford a separate robot only for use in a skills laboratory. 
 
In these cases, the available robot in the operating room could be used for training after working hours, or at scheduled times when no surgery is being performed. Training on animal and cadaver simulation models has the advantage of simulating the human anatomy and this kind of training has been incorporated in several robotic training courses. These courses seem to enable participants to successfully incorporate robot-assisted surgery and maintain this technique in clinical practice in the short- and long-term.5,6
 
Although animal models provide a simulation experience comparable to real patients, resource limitation and ethical concerns can be a deterrent in the wider application of these training methods. A way to overcome these limitations is a VR simulator, which can be used to train this new surgical technique before applying robotic surgery on patients.
 
When using simulation models for surgical training, it is important that the training exercises are validated and have a proper goal. The trainee should know what to train and when proficiency is reached. Several levels of validity can be distinguished with respect to medical simulators (Table 1). 
 
 
Simulator exercises should at least have good face- and construct validity, and translate well to the clinical setting before they can be used in a robotic training programme. Unfortunately there are currently only a few published reports of validated exercises to be used within a dry-lab skills-laboratory, but there are currently no commercially available validated exercises for dry-lab training. However, a number of validation reports of virtual reality simulators have been published in the medical literature in recent years. 
 
Virtual reality training
VR simulation will most likely play a very important role in training and education in robotic surgery. In laparoscopy, surgical skills training in a virtual environment had a significant learning effect and the learned skills were consistent with, and transferable to, actual procedures.7 There is a rapidly growing body evidence that this will also be the case for robotic surgery. Depending on budget and training purposes, several simulators are now commercially available. The SEP Robot™ simulator (SimSurgery AS, Oslo, Norway) is part of a conventional VR trainer for laparoscopy, which can be converted into a simulator for robotic surgery. Concepts of face and construct validity for this simulator are not strong.8,9
 
Two more advanced VR simulators are especially designed for robot assisted surgery. The Robotic Surgical Simulator (RoSS™) (Simulated Surgical Systems, Williamsville, NY, USA) was developed through collaboration between the Roswell Park Cancer Institute and the University of Buffalo. This simulator demonstrated face and content validity.10,11 Currently the most distributed VR trainer for robotic surgery is the dV-Trainer™ (dVT) (Mimic Technologies, Seattle, WA, USA). This is a simulator which uses the same kinematics as the da Vinci® Surgical System (dVSS). The da Vinci Skills Simulator™ was released by Intuitive Surgical in 2011. This simulator contains Mimic’s dVT software loaded on computer hardware that can be attached to the dVSS surgeon’s console. These last two systems will be discussed in more detail because they are currently the most used VR training systems for robotic surgery worldwide.
 
dV-Trainer™ and the da Vinci Skills Simulator™
The dVT is a standalone surgical simulator that is especially designed for training robotic surgery, and closely reproduces the dVSS behaviour (Figure 1). The simulator consists of a separate console and a foot pedal identical to the real robot. The software platform has two main training modules. The first contains system training for the dVSS combined with basic surgical skills training and the second contains advanced surgical skills training. Both modules contain four exercise categories, each with a number of different exercises. In this way it is suitable for both beginners and more experienced surgeons to improve their specific robotic skills.
 
Fig. 1: Virtual reality training, the dVT-Trainer™.
 
A number of validation studies with respect to this simulator have been published over the past years. Several validation studies demonstrated good face-, content- and construct validity during the development phase of this system.12–15 The simulator seems to provide almost equal training capacities compared with the real dVSS.16 Moreover, training on the dVT can actually improve performance on the robot system equal to training with the robot itself. Improvement of technical surgical performance can be achieved within a relatively short period of time.17,18 Because the basic forms of validity and the learning capacity of the dVT are well established, the question arises if this VR system could also be used for assessment of robotic skills.19 Perrenot et al concluded in their study that the dVT proves to be a valid tool to assess basic skills of robotic surgery on the dVSS.20
 
In 2011, the dVT software exercises became commercially available for use directly on the da Vinci® Si console with the release of the da Vinci® Skills Simulator™. The hardware is attached to the actual robotic system as a separate box, or ‘backpack’. The box contains the software and can be used with the new Si robot models as an add-on tool. In this way, virtual training on the actual robotic console is possible (Figure 2). The first validation studies for the da Vinci® Skills Simulator™ were published in 2011 and demonstrated good face-, content- and construct validity.21,22 A recent prospective randomised study by Hung et al.23 demonstrated the most ultimate forms of validity (concurrent and predictive validity) for the da Vinci® Skills Simulator™. The study showed that a simulator-trained group performed better in real surgery on the dVSS.23
 
Fig. 2: Da Vinci Skills Simulator™.
 
The dVT uses several parameters to measure performance of the trainee. Objective skills assessment is possible and the new scoring system (Mscore®) is based on the mean and standard deviation data of more then 100 experienced surgeons. In this way, the trainee can actually train to an expert level of robotic skills before starting to operate on patients. In a direct comparison between the dVT and the da Vinci Skills Simulator™, both simulators appear to be equivalent for assessment of surgical proficiency, and either device can be used for robotic skills training with self-assessment feedback.24
 
Mimic Technologies produce Msim 2.0™, a simulation platform for the dVT that increases the realism and produce more lifelike training scenarios. The new platform will introduce suturing and knot tying exercises that will be included in the ‘basic’ dVT simulation offering. The company has also announced plans to develop procedure-specific training content for urology, gynaecology and general surgery based on technology that combines 3D video and virtual reality. The dVT seems to offer the most advanced tools for curriculum building, customisation and sharing, including an online portal where institutions can upload and share their curriculum and supporting documents for others to freely use on their own simulators. 
 
With the wider implementation of robotic surgery, more training facilities are required. The official Mimic Medical Education and Development (MimicMED™) VR training centre was opened at the Florida Hospital Nicholson Centre, Celebration, Florida. This centre has 10 dVT virtual reality simulators that are used for a wide variety of robotic training courses for residents, fellows and medical specialists.
 
Conclusions 
Robot-assisted laparoscopic surgery is one of the main future directions of minimally invasive laparoscopic surgery. With the exponential growth of robotic surgery, the need for competence-based training curricula for residents, fellows and surgeons is increasing rapidly. However, there is still a lack of validated training tools for robot-assisted laparoscopic surgery and more research in this field needs to be carried out in the near future. 
 
With the quickly improving quality of virtual reality simulators for robotic surgery, it is expected that this training modality will play an important role in training, assessment and credentialling of future surgeons. The dV-Trainer™ and the da Vinci® Skills Simulator™ are currently the most widely accepted VR trainers for robotic surgery. Robotic training should be organised preferably in high volume centres through the implementation of a systematic and structured competence-based training programme. 
 
In this way, the acquisition of skills needed for robotic surgery can be acquired in a safe and cost effective way. This has the potential to increase patient safety and surgical performance for the growing number of clinicians undertaking robotic surgery.
 
References
  1. Schreuder HWR, Verheijen RHM. Robotic surgery. BJOG 2009;116:198–213.
  2. Santos-Carreras L et al. Survery on surgical instrument handle design: ergonomics and acceptance. Surg Innov 2012;19:50–9.
  3. Steinberg PL et al. Cost of learning robotic-assisted prostatectomy. Urology 2008;72:1068–72.
  4. Schreuder HWR et al. Training and learning robotic surgery. Time for a more structured approach: a systematic review. BJOG 2012;119:137–49.
  5. Gamboa AJ et al. Long-term impact of a robot assisted laparoscopic prostatectomy mini fellowship training program on postgraduate urological practice patterns. J Urol 2009;181:778–82. 
  6. McDougall EM et al. Short-term impact of a robot-assisted laparoscopic prostatectomy ‘mini-residency’ experience on postgraduate urologists’ practice patterns. Int J Med Robot 2006;2:70–4.
  7. Larsen CR et al. Effect of virtual reality training on laparoscopic surgery: a randomised controlled trial. BMJ 2009;338:b1802.
  8. Gavazzi A et al. Face, content and construct validity of virtual reality simulator for robotic surgery (SEP Robot). Ann R Coll Surg Engl 2011;93:152–6.
  9. van der Meijden OA, Broeders IA, Schijven MP. The SEP “robot”: A valid virtual Reality robotic simulator for the Da Vinci surgical system? Surg Technol Int 2010;19:51–8.
  10. Seixas-Mikelus SA et al. Face validation of a novel robotic surgical simulator. Urology 2010;76:357–60.
  11. Seixas-Mikelus SA et al. Content validation of a novel robotic surgical simulator. BJU Int 2011;107:1130–5.
  12. Lendvay TS et al. Initial validation of a virtual-reality robotic simulator. J Robotic Surg 2008;2:145–9. 
  13. Kenney PA et al. Face, content, and construct validity of DV-Trainer, a novel virtual reality simulator for robotic surgery. Urology 2009;73:1288–92.
  14. Sethi AS et al. Validation of a novel virtual reality robotic simulator. J Endourol 2009;23:503-8.H
  15. Lendvay TS et al. VR robotic surgery: Randomized blinded study of the DV-trainer robotic simulator. Stud Health Technol Inform 2008;132:242–4. 
  16. Lerner MA et al. Does training on a virtual reality robotic simulator improve performance on the Da Vinci surgical system? J Endourol 2010;24:467–72.
  17. Kang SG et al. A study on the learning curve of the robotic virtual reality simulator. J Laparoendosc Adv Surg Tech 2012;22:438–62.
  18. Korets R et al. Validating the use of the Mimic DV-Trainer for robotic surgery skill acquisition among urology residents. Urology 2011;78:1326–30.
  19. Lee JY et al. Validation study of a virtual reality robotic simulator – role as an assessment tool? J Urol 2012;187:998–1002.
  20. Perrenot C et al. The virtual reality simulator DV-Trainer((R)) is a valid assessment tool for robotic surgical skills. Surg Endosc 2012;26:2587–93.
  21. Hung AJ et al. Face, content and construct validity of a novel robotic surgery simulator. J Urol 2011;186:1019–24.
  22. Finnegan KT et al. Da Vinci Skills Simulator construct validation study: Correlation of prior robotic experience with overallscore and score simulator performance. Urology 2012;80:330–6.
  23. Hung AJ et al. Concurrent and predictive validation of a novel robotic surgery simulator: a prospective, randomized study. J Urol 2012;187:630–7. 
  24. Liss MA et al. Validation, correlation, and comparison of the da Vinci Trainer™ and the da Vinci Surgical Skills Simulator™ using the Mimic™ software for urologic robotic surgical education. J Endourol 2012.