Design and development of a high-fidelity transrectal ultrasound (TRUS) simulation model for remote education and training

Introduction and objective: Transrectal ultrasound (TRUS) biopsy teaches the basics of ultrasound guided techniques while also providing a basis for more advanced prostate oncology diagnosis methods. TRUS can cause discomfort to patients and requires skills in three-dimensional orientation and interpretation of findings. We sought to design a high-fidelity simulation model for resident education which can be used to further TRUS training techniques. Furthermore, Mixed reality (MR) allows the fusion of two video streams allowing real time overlay of a remote instructors’ hands onto the trainee's view. We further aim to evaluate remote MR training compared to in person (IP) training using a validated TRUS-Bx hydrogel simulation model. Methods: Validation was completed in 3 phases: Phase 1, Delphi methodology to gain consensus from an expert panel of endourologists. Consensus (>80% agreement) over 3 rounds defined 81 essential elements. Phase 2, Prototype development: these essential items were incorporated into prototypes fabricated using a combination of hydrogel molding and 3D printing. Phase 3: Validation comparing 6 experts and 6 novices performance from 4 centers using the consensus based objective and subjective metrics. Following this,14 participants with <5 case experience were randomized into MR and IP arms to reviewed educational videos of relevant anatomy and TRUS-BX steps prior to completing a pre-test, 3 training sessions, and post-test. In pre- and post-test participants independently measured the prostate, administered anesthetic and completed 14 biopsies on a validated hydrogel model with each biopsy area colored separately. Accuracy was defined as percentage of each core with the correct color corresponding to biopsy area. In training sessions faculty guided trainees through the procedure steps on a non-colored model either remotely using a MR platform or in person. MR set up included transmitting ultrasound view and audio via Zoom and a tablet displaying the merged surgical field with proctor hands. The remote faculty annotated the ultrasound view and guided trainees with their hands using the merged surgical view. Post-training surveys evaluated trainee perceptions and proctor assessment. Results: When asked how the model replicates the relevant human anatomy for the procedure, experts and novices rated the model 3.75/5 and 4.5/5 respectively. Additionally, both rated the model >4 when asked if the overall simulated tissue accurately resembles the appearance of live human tissue. Furthermore, both groups rated the model highly (≥4) for the procedural realism. When asked about teaching using the model, experts and novices rated the model highly (≥4) agreeing that the model is useful for improving technical skills, teaching the procedure, and assessing the user's ability to perform the procedure. Experts took significantly less attempts and time per biopsy region, less time per attempt, and reported significantly lower difficulty than novices (2.4 v 3.7, p = 0.001; 59.8 v 123.9, p < 0.001; 23.3 v 31.3, p = 0.001; 3.0 v 4.8, p = 0.001, respectively). However, both groups best core accuracy in each region was similar for all attempts (88% v 92%, p = 0.31). In our comparative study, participants reported equal confidence in knowledge (MR: 80.6/100 vs IP: 87.8/100, p = 0.49), ability to perform simulated TRUS (89.8 vs 90, p == 0.97) and live TRUS (66.8 vs 71, p == 0.73) on completion. Pre-test core percentage was similar (MR: 17.9% vs IP: 26.4%, p == 0.44). Both groups experienced significant increase in post-test scores (75.9 and 62.3% for MR and IP group respectively).MR groups increase was x1.5 times greater (MR: +58.0%, p < 0.01, IP: +35.9%, p < 0.01) despite trainee perceptions that remote training may hinder their ability to learn. Faculty rated the trainee skills from 1 (below expectations) to 3 (exceeds expectations) at TRUS manipulation, measurement, anesthetic, and biopsy. The MR group averaged 0.5, 0.1, 0.8 and 0.5 higher respectively. Conclusions: This TRUS biopsy model incorporates essential components for inclusion in resident training curriculum as a teaching and assessment tool by providing instantaneous feedback and procedural metrics while displaying high anatomical and procedural realism ratings. Ultimately this model can be utilized for virtual learning, utilizing its portable and non-biohazardous properties in combination with merged reality software that has been proven to be equivalent learning to in-person simulation training. This technology has the potential for cross-institutional training. Further studies will seek to increase our sample size and obtain external validation.