Feasibility, effectiveness and transferability of a novel mastery-based virtual reality robotic training platform for general surgery residents

Abstract

Background The annual number of robotic surgical procedures is on the rise. Robotic surgery requires unique skills compared to other surgical approaches. Simulation allows basic robot skill acquisition and enhances patient safety. The purpose of this study was to evaluate the feasibility, effectiveness, and transferability of a mastery-based curriculum using a new virtual reality (VR) robotic simulator for surgery resident training. Methods Nineteen PGY2s and 22 PGY4s were enrolled. Residents completed a pretest and posttest consisting of five VR and three previously validated inanimate tasks. Training included practicing 33 VR tasks until a total score ≥ 90% (“mastery”) was achieved using automated metrics (time, economy of motion). Inanimate performance was evaluated by two trained, blinded raters using video review metrics (time, errors, and modified OSATS). Outcomes were defined as: curriculum feasibility (completion rate, training time, repetitions), training effectiveness (pre/post training skill improvement), and skill transferability (skill transfer to validated inanimate drills). Wilcoxon signed-rank and Mann–Whitney U tests were used; median (IQR) reported. Results Thirty-four of 41 residents (83%) achieved mastery on all 33 VR tasks; median training time was 7 h (IQR: 5′26″–8′52″). Pretest vs. post-test performance improved (all p < 0.001) according to all VR and Inanimate metrics for both PGY2 and PGY4 residents. Significant pretest performance differences were observed between PGY2 and PGY4 residents for VR but not inanimate tasks; no PGY2 vs. PGY4 posttest performance differences were observed for both VR and inanimate tasks. Conclusion This mastery-based VR curriculum was associated with a high completion rate and excellent feasibility. Significant performance improvements were noted for both the VR and inanimate tasks, supporting training effectiveness and skill transferability. Additional studies examining validity evidence may help further refine this curriculum.

Advances in Virtual Reality and 3d Printing Technologies Applied to General Surgery: A Literature Review

“…It is important to emphasize the increase in patient safety undergoing a given procedure, since performing the surgical technique in the field is preceded by training in realistic simulation, resulting in reduced surgical time and blood loss (RADI et al, 2022). In this context, 3D printing and virtual reality technologies can be combined, allowing the association of models and virtual environments with physical structures, which contributes to better training of surgeons and planning of the operation to be performed (CORNEJO et al, 2022).…”

“…There is a positive impact on the safety of patients undergoing specific procedures, since performing the surgical technique in the field is preceded by training in realistic simulation, resulting in reduced surgical time and blood loss (RADI et al, 2022). In this context, the combination of 3D printing and virtual reality technologies enables the integration of models and virtual environments with physical structures, providing a significant improvement in surgeon training and operation planning (CORNEJO et al, 2022).…”

Training in robotic pancreatic surgery

“…Following formal training programmes, a steep increase in the use of MIS has been seen, with reduced blood loss and conversion rates [35] . Specific to RPD, a five-step proficiency-based robotic curriculum has been described by the University of Pittsburgh Medical Center [36,37] . The five steps are (1) a proficiency-based virtual reality simulation curriculum; (2) an inanimate bio-tissue curriculum; (3) video library training; (4) an intra-operative evaluation; and (5) skill maintenance with ongoing assessment.…”

In silico simulation: a key enabling technology for next-generation intelligent surgical systems

“…In the context of MIS, numerous simulation frameworks have been developed by researchers either with machine learning specifically in mind [28,44,45] or as training tools for clinicians and subsequently adapted for machine learning applications [29,37]. One of the most mature examples is the SimNow software suite, developed for the da Vinci Surgical Robot, which includes training routines for 33 surgical skills [46], as well as complete procedures (Intuitive Surgical Operations, Inc.; Sunnyvale, California). The increasing maturity of this and other simulation frameworks for MIS has heightened interest in in silico training for intelligent systems as well as clinicians.…”