Design and Validation of a Low-Cost, High-Fidelity Model for Urethrovesical Anastomosis in Radical Prostatectomy

Evaluating robotic-assisted partial nephrectomy surgeons with fully convolutional segmentation and multi-task attention networks

We use machine learning to evaluate surgical skill from videos during the tumor resection and renography steps of a robotic assisted partial nephrectomy (RAPN). This expands previous work using synthetic tissue to include actual surgeries. We investigate cascaded neural networks for predicting surgical proficiency scores (OSATS and GEARS) from RAPN videos recorded from the DaVinci system. The semantic segmentation task generates a mask and tracks the various surgical instruments. The movements from the instruments found via semantic segmentation are processed by a scoring network that regresses (predicts) GEARS and OSATS scoring for each subcategory. Overall, the model performs well for many subcategories such as force sensitivity and knowledge of instruments of GEARS and OSATS scoring, but can suffer from false positives and negatives that would not be expected of human raters. This is mainly attributed to limited training data variability and sparsity.

3D printed patient-specific prostate cancer models to guide nerve-sparing robot-assisted radical prostatectomy: a systematic review

Precise knowledge of each patient's index cancer and surrounding anatomy is required for nerve-sparing robot-assisted radical prostatectomy (NS-RARP). Complementary to this, 3D printing has proven its utility in improving the visualisation of complex anatomy. This is the first systematic review to critically assess the potential of 3D printed patient-specific prostate cancer models in improving visualisation and the practice of NS-RARP. A literature search of PubMed and OVID Medline databases was performed using the terms "3D Printing", "Robot Assisted Radical Prostatectomy" and related index terms as per the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Eight articles were included; six were identified via database searches, to which a further two articles were located via a snowballing approach. Eight papers were identified for review. There were five prospective single centre studies, one case series, one technical report and one letter to the editor. Of these articles, five publications (62.5%) reported on the utility of 3D printed models for NS-RARP planning. Two publications (25%) utilised 3D printed prostate models for simulation and training, and two publications (25%) used the models for patient engagement. Despite the nascency of the field, 3D printed models are emerging in the uro-oncological literature as a useful tool in visualising complex anatomy. This has proven useful in NS-RARP for preoperative planning, simulation and patient engagement. However, best practice guidelines, the future regulatory landscape, and health economic considerations need to be addressed before this synergy of new technologies is ready for the mainstream.

Simulation-based education in urology - an update

Over the past 30 years surgical training, including urology training, has changed from the Halstedian apprenticeship-based model to a competency-based one. Simulation-based education (SBE) is an effective, competency-based method for acquiring both technical and non-technical surgical skills and has rapidly become an essential component of urological education. This article introduces the key learning theory underpinning surgical education and SBE, discussing the educational concepts of mastery learning, deliberate practice, feedback, fidelity and assessment. These concepts are fundamental aspects of urological education, thus requiring clinical educators to have a detailed understanding of their impact on learning to assist trainees to acquire surgical skills. The article will then address in detail the current and emerging simulation modalities used in urological education, with specific urological examples provided. These modalities are part-task trainers and 3D-printed models for open surgery, laparoscopic bench and virtual reality trainers, robotic surgery simulation, simulated patients and roleplay, scenario-based simulation, hybrid simulation, distributed simulation and digital simulation. This article will particularly focus on recent advancements in several emerging simulation modalities that are being applied in urology training such as operable 3D-printed models, robotic surgery simulation and online simulation. The implementation of simulation into training programmes and our recommendations for the future direction of urological simulation will also be discussed.

Development of a novel 3D-printed and silicone live-wire model for thyroidectomy

PURPOSE

We present the development and validation of a novel and innovative low-cost model for thyroidectomy. The purpose is to provide a high-fidelity and inexpensive method to provide repetition to surgeons early on the learning curve.

MATERIALS AND METHODS

The model consists of a 3D-printed laryngeal and tracheal framework, with silicone components to replicate the thyroid gland, strap muscles, and skin. A copper wire models the recurrent laryngeal nerve and is circuited with a buzzer to indicate contact with instruments. Thirteen resident trainees successfully completed the simulated thyroidectomy after viewing an instructional video. Face validity of the model was assessed with a 19-item 5-point Likert scale survey. Subject performance was assessed using a checklist of procedure steps.

RESULTS

Participant feedback indicated enthusiasm for realism of the recurrent nerve (4.46 average Likert rating, 5 indicates strong agreement), dissection of the nerve (4.15), use of the buzzer (4.69), and overall satisfaction (4.46). Soft tissue components scored poorly including realism of the skin (3.08), thyroid gland (3.31), and mobilization of the lobe (3.23), identifying aspects to improve. All participants reported increased confidence with thyroid surgery after using the model; this was most pronounced among junior residents (1.5 ± 0.76 versus 3.13 ± 1.13; p = 0.016).

CONCLUSION

Thyroidectomy requires repetition and volume to gain competence. Use of the simulator early in training will provide confidence and familiarity, to enhance the educational value of subsequent live surgery.

Multimodal In-training Examination in an Emergency Medicine Residency Training Program: A Longitudinal Observational Study

Background

In-training examination (ITE) has been widely adopted as an assessment tool to measure residents' competency. We incorporated different formats of assessments into the emergency medicine (EM) residency training program to form a multimodal, multistation ITE. This study was conducted to examine the cost and effectiveness of its different testing formats.

Methods

We conducted a longitudinal study in a tertiary teaching hospital in Taiwan. Nine EM residents were enrolled and followed for 4 years, and the biannual ITE scores were recorded and analyzed. Each ITE consisted of 8–10 stations and was categorized into four formats: multiple-choice question (MCQ), question and answer (QA), oral examination (OE), and high-fidelity simulation (HFS) formats. The learner satisfaction, validity, reliability, and costs were analyzed.

Results

486 station scores were recorded during the 4 years. The numbers of MCQ, OE, QA, and HFS stations were 45 (9.26%), 90 (18.5%), 198 (40.7%), and 135 (27.8%), respectively. The overall Cronbach's alpha reached 0.968, indicating good overall internal consistency. The correlation with EM board examination was highest for HFS (ρ = 0.657). The average costs of an MCQ station, an OE station, and an HFS station were ~3, 14, and 21 times that of a QA station.

Conclusions

Multi-dimensional assessment contributes to good reliability. HFS correlates best with the final training exam score but is also the most expensive format among ITEs. Increased testing domains with various formats improve ITE's overall reliability. Program directors must understand each test format's strengths and limitations to bring forth the best combination of exams under the local context.

Evaluating robotic-assisted surgery training videos with multi-task convolutional neural networks

We seek to understand if an automated algorithm can replace human scoring of surgical trainees performing the urethrovesical anastomosis in radical prostatectomy with synthetic tissue. Specifically, we investigate neural networks for predicting the surgical proficiency score (GEARS score) from video clips. We evaluate videos of surgeons performing the urethral anastomosis using synthetic tissue. The algorithm tracks surgical instrument locations from video, saving the positions of key points on the instruments over time. These positional features are used to train a multi-task convolutional network to infer each sub-category of the GEARS score to determine the proficiency level of trainees. Experimental results demonstrate that the proposed method achieves good performance with scores matching manual inspection in 86.1% of all GEARS sub-categories. Furthermore, the model can detect the difference between proficiency (novice to expert) in 83.3% of videos. Evaluation of GEARS sub-categories with artificial neural networks is possible for novice and intermediate surgeons, but additional research is needed to understand if expert surgeons can be evaluated with a similar automated system.

A review of simulation training and new 3D computer-generated synthetic organs for robotic surgery education

We conducted a comprehensive review of surgical simulation models used in robotic surgery education. We present an assessment of the validity and cost-effectiveness of virtual and augmented reality simulation, animal, cadaver and synthetic organ models. Face, content, construct, concurrent and predictive validity criteria were applied to each simulation model. There are six major commercial simulation machines available for robot-assisted surgery. The validity of virtual reality (VR) simulation curricula for psychomotor assessment and skill acquisition for the early phase of robotic surgery training has been demonstrated. The widespread adoption of VR simulation has been limited by the high cost of these machines. Live animal and cadavers have been the accepted standard for robotic surgical simulation since it began in the early 2000s. Our review found that there is a lack of evidence in the literature to support the use of animal and cadaver for robotic surgery training. The effectiveness of these models as a training tool is limited by logistical, ethical, financial and infection control issues. The latest evolution in synthetic organ model training for robotic surgery has been driven by new 3D-printing technology. Validated and cost-effective high-fidelity procedural models exist for robotic surgery training in urology. The development of synthetic models for the other specialties is not as mature. Expansion into multiple surgical disciplines and the widespread adoption of synthetic organ models for robotic simulation training will require the ability to engineer scalability for mass production. This would enable a transition in robotic surgical education where digital and synthetic organ models could be used in place of live animals and cadaver training to achieve robotic surgery competency.

Development and Validation of a Novel Surgical Simulation for Parotidectomy and Facial Nerve Dissection

We present the development and validation of a low-cost novel model for training of parotid surgery. The model consists of a 3-dimensionally printed skeleton, silicone-based soft tissue, and facial nerve replicated with copper wire, circuited to indicate contact with instruments. The face validity of the simulator was evaluated with a 21-item 5-point Likert survey. Content validity was evaluated through a survey completed by the trainees after their first live parotidectomy following the simulation. Twelve residents and 6 faculty completed the simulated procedure of superficial parotidectomy after watching a video demonstration. Completion of 16 surgical steps evaluated by this model was graded for each participant. The mean ± SD total assessment score for faculty was 15.83 ± 0.41, as compared with 13.33 ± 2.06 for residents (P = .0081). The simulator as a training tool was well received by both faculty and residents (5 vs 4, P = .0206). Participants strongly agreed that junior residents would benefits from use of the model.

Current Status of Simulation Training in Urology: A Non-Systematic Review

Simulation has emerged as an effective solution to increasing modern constraints in surgical training. It is recognized that a larger proportion of surgical complications occur during the surgeon's initial learning curve. The simulation takes the learning curve out of the operating theatre and facilitates training in a safe and pressure-free environment whilst focusing on patient safety. The cost of simulation is not insignificant and requires commitment in funding, human resources and logistics. It is therefore important for trainers to have evidence when selecting various simulators or devices. Our non-systematic review aims to provide a comprehensive up-to-date picture on urology simulators and the evidence for their validity. It also discusses emerging technologies and future directions. Urologists should embed evidence-based simulation in training programs to shorten learning curves while maintaining patient safety and work should be directed toward a validated and agreed curriculum.