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ROBOTICS Emerging Trends That Herald the Future of Robotic Surgical Simulation

By: Ahmed Ghazi, MD, FEBU, MHPE, Brady Urological Institute, The Johns Hopkins University, Baltimore, Maryland | Posted on: 01 Mar 2024

Robotic surgery is one of the most technically demanding fields that warrants a high level of expertise. In the present context of high societal expectations regarding quality of patient care and medicolegal and financial constraints, there are fewer opportunities to achieve competency in robotic urology operative techniques. Practice on cadavers as “the ultimate anatomical simulator” has been a trend since development of the first generation of surgical robots; however, ethical concerns, rising costs, operating in a bloodless field, and the need for specialized facilities has relegated it to footnote status for robotic simulation. The development of sophisticated virtual reality (VR) simulators with their automated computer-generated metrics was thought to be the final panacea; however, their lack of realistic surgical interfaces and tissue modeling, poor signal processing for complex events associated with surgery, and clinically irrelevant metrics have limited them to the initial phase of robotic training. This conundrum has left stakeholders with limited options on the ideal platform for robotic simulation that can realistically mitigate the burden of operative patient training. This article will focus on current emerging trends; three-dimensional printing including patient specific simulation, automatically generated clinically relevant metrics, and single-port (SP) robotic training.

The development of realistic physical models with strategic modifications at the Patrick C. Walsh Discovery and Learning Laboratory, Johns Hopkins Brady Urological Institute have given simulation education a new dimension.1 A technique combining image segmentation, 3D printing technology, and polymer molding to create an immersive, procedural simulation platform for robotic urologic procedures has opened a wide arena for the development of high-fidelity models for robotic urology training. This molding technique allows different materials replicating the various mechanical properties of human tissue to be layered into a single model.2 For full immersion, the fabrication process also incorporates full procedure practice by the addition of surrounding organs and reproducing genuine operative metrics of performance (blood loss, tumor margins, ischemia time, urine leak, and the potential for complications), enabling practicing surgeons to obtain feedback and track performance. These features, which we have collective referred to as “physical reality,” set this approach apart from any other simulation platforms that create realistic models to be used in training of complex urologic procedures.

Recently, the concept of patient-specific simulation as a strategy marks a distinct shift in the use of simulation from a platform that allows practice of a specific skill (ie, training) to one that allows cognitive and/or physical rehearsal of a specific event (ie, a patient’s operation). Patient-specific simulation in any form allows surgeons to cognitively or physically practice, plan, and address potential problems related to a specific patient’s case, thus optimizing the real intervention.3 The benefits of this concept were demonstrated for robotic management of complex renal masses that would otherwise not undergo a nephron-sparing approach4 and is currently being developed for robot-assisted radical prostatectomy (RARP; Figure 1).


Figure 1. Patient-specific partial nephrectomy perfused hydrogel model. Computer design resulting from segmentation of patient CT scan (A); serial 3D-printed casts (tumor, renal hilum, renal parenchyma) with hydrogel kidney model containing tumor, renal vasculature, and major vessels (B);verification of the anatomical accuracy of the model in comparison to patient CT scan (C); mechanical testing of hydrogel to replicate human tissue (D); full procedure practice platform by the addition of surrounding organs (E); patient-specific simulation demonstrates excision of a tumor with bleeding (F; left live surgery, right simulated rehearsal).

One of the most important advantages of surgical simulators is the opportunity they afford to acquire skills, gain confidence, and experience success before working with real patients, especially when the user’s clinical exposure is limited. However, if the simulator does not provide useful instructional feedback to the user, this advantage is significantly blunted by the need for an instructor to supervise and tutor the trainee while using the simulator. Thus, the incorporation of relevant, intuitive metrics is essential for the development of efficient simulators. Equally as important is the presentation of such metrics to the user in such a way so as to provide constructive feedback that facilitates independent learning and improvement. From design to conception, clinically relevant objective metrics pertinent to the procedure were incorporated into the Walsh lab RARP5 and partial nephrectomy6 models as a means of quantitative method for assessment of surgical performance. Metrics included positive tumor margins, blood loss, and sensors that measure degree of tension on sensitive tissues (eg, neurovascular bundle) that could differentiate between various experience levels. Uniquely, a data set of these metrics collected from 35 expert urologists at the 2022 AUA conference were analyzed using a supervised machine learning and could accurately predict caseload with 96% AUC.7 This dataset is currently part of a mastery registry used to extract the essence of an expert’s skillful maneuvers during nerve-sparing radical prostatectomy as a roadmap for novice learners. Crucially, researchers have demonstrated a correlation between VR simulation performance and live surgical RARP performance in the real operative environment thereby increasing its validity as a training modality. One study of 20 surgeons (14 of whom were experts) demonstrated a statistically significant correlation between VR needle driving scores and contingency recovery at 24 months after real RARP cases, with needle driving scores on the simulator correlating with live operative needle driving scores.8 Another study reported similar findings9 noting a positive association for expert surgeons for VR needle hold angle and driving smoothness skills and continence recovery at 3 months. Given that such technical skills influence postoperative outcomes, these findings could point towards VR being not only a training tool but also a key assessor of technical performance.


Figure 2. Fabricated models corresponding to the consensus-driven single-port (SP) specific skills. MP indicates multiport robotic surgery.

To date, there has been no coordination of available curricula, and the result is that many different curricula (with different outcome measures) exist for the same procedures. In addition, there is no uniform method for developing a curriculum. Why a trainee at one institution should have a completely different education and different assessment criteria than another at a different institution for the same surgical procedures is incomprehensible in the current data-sharing digital era. Using an Educational Design Framework,10 Kern’s 6-step framework for curriculum development, a comprehensive curriculum for recently introduced SP robot was developed. Twenty-two experts were invited to participate in a Delphi consensus-building approach regarding a needs assessment, components of a simulation-based SP curriculum, and assessment of surgical performance. The final curriculum included an online didactic platform with a video library, skills training platforms, post-curriculum proctoring, and evaluation to assess the transfer of skill to live cases. Expert consensus identified 11 basic and 7 advanced SP-specific skills critical to adoption of SP robotics. Using 3D printing and hydrogel casting, 6 hydrogel partial tasks and 4 procedures11 were fabricated to address SP-specific skills and provide comprehensive skills training (Figure 2). Following preliminary validation this comprehensive curriculum will be implemented at the first SP masterclass hands-on training, May 2, 2024, at the AUA Annual Meeting in San Antonio, Texas, for the first 20 registrants.

  1. Ghazi A. A call for change. Can 3D printing replace cadavers for surgical training?. Urol Clin North Am. 2022;49(1):39-56.
  2. Melnyk R, Ezzat B, Belfast E, et al. Mechanical and functional validation of a perfused, robot-assisted partial nephrectomy simulation platform using a combination of 3D printing and hydrogel casting. World J Urol. 2020;38(7):1631-1641.
  3. Ghazi A, Saba P, Melnyk R, Joseph J. Utilizing 3D printing and hydrogel casting for the development of patient-specific rehearsal platforms for robotic assisted partial nephrectomies. Urology. 2021;147:317.
  4. Ghazi A, Shepard L, Schuler N, Saba P, Joseph J. The development & implementation of a 3D printing perfused hydrogel robotic assisted partial nephrectomy surgical training platform: advancing from generic to patient specific simulation-based translational research. Urol Video J. 2023;17:100205.
  5. Witthaus MW, Farooq S, Melnyk R, et al. Incorporation and validation of clinically relevant performance metrics of simulation (CRPMS) into a novel full-immersion simulation platform for nerve-sparing robot-assisted radical prostatectomy using three-dimensional printing and hydrogel molding technology. BJU Int. 2020;125(2):322-332.
  6. Ghazi A, Melnyk R, Hung A, et al. Multi-institutional validation of a perfused robot-assisted partial nephrectomy procedural simulation platform utilizing clinically relevant objective metrics of simulators (CROMS). BJU Int. 2021;127(6):645-653.
  7. Schuler N, Shepard L, Saxton A, et al. Predicting surgical experience after robotic nerve-sparing radical prostatectomy simulation using a machine learning-based multimodal analysis of objective performance metrics. Urol Pract. 2023;10(5):447-455.
  8. Chu TN, Wong EY, Ma R, et al. A multi-institution study on the association of virtual reality skills with continence recovery after robot-assisted radical prostatectomy. Eur Urol Focus. 2023;S2405-4569(23)00122-0.
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  10. Melnyk R, Saba P, Holler T, et al. Design and implementation of an emergency undocking curriculum for robotic surgery. Sim Healthc. 2022;17(2):78-87.
  11. Schuler N, Shepard L, Holler T, et al. Pilot evaluation of a perfused robot-assisted partial nephrectomy procedural simulation platform for single port robotic retroperitoneal approaches. Urol Video J. 2023;18:100225.