Screening surgical residents’ laparoscopic skills using virtual reality tasks: Who needs more time in the sim lab?

Creating a Successful Virtual Reality-Based Medical Simulation Environment: Tutorial

Innovation in medical education is not only inevitable but a requirement. Manikin-based simulation is currently the gold standard for supplemental clinical training; however, this modality requires significant equipment and personnel to operate. Virtual reality (VR) is emerging as a new method of delivering medical simulation sessions that requires less infrastructure but also allows for greater accessibility and flexibility. VR has slowly been integrated into the medical curriculum in some hospitals; however, more widespread adoption would transform the delivery of medical education for future clinicians. This tutorial introduces educators to the BUILD REALITY (begin, use, identify, leverage, define, recreate, educate, adapt, look, identify, test, amplify) framework, a series of practical tips for designing and implementing a VR-based medical simulation environment in their curriculum. The suggestions are based on the relevant literature and the authors' personal experience in creating and implementing VR environments for medical trainees. Altogether, this paper provides guidance on conducting a needs assessment, setting objectives, designing a VR environment, and incorporating the session into the broader medical curriculum.

Can Deep Learning Algorithms Help Identify Surgical Workflow and Techniques?

BACKGROUND

Surgical videos are now being used for performance review and educational purposes; however, broad use is still limited due to time constraints. To make video review more efficient, we implemented Artificial Intelligence (AI) algorithms to detect surgical workflow and technical approaches.

METHODS

Participants (N = 200) performed a simulated open bowel repair. The operation included two major phases: (1) Injury Identification and (2) Suture Repair. Accordingly, a phase detection algorithm (MobileNetV2+GRU) was implemented to automatically detect the two phases using video data. In addition, participants were noted to use three different technical approaches when running the bowel: (1) use of both hands, (2) use of one hand and one tool, or (3) use of two tools. To discern the three technical approaches, an object detection (YOLOv3) algorithm was implemented to recognize objects that were commonly used during the Injury Identification phase (hands versus tools).

RESULTS

The phase detection algorithm achieved high precision (recall) when segmenting the two phases: Injury Identification (86 ± 9% [81 ± 12%]) and Suture Repair (81 ± 6% [81 ± 16%]). When evaluating three technical approaches in running the bowel, the object detection algorithm achieved high average precisions (Hands [99.32%] and Tools [94.47%]). The three technical approaches showed no difference in execution time (Kruskal-Wallis Test: P= 0.062) or injury identification (not missing an injury) (Chi-squared: P= 0.998).

CONCLUSIONS

The AI algorithms showed high precision when segmenting surgical workflow and identifying technical approaches. Automation of these techniques for surgical video databases has great potential to facilitate efficient performance review.

Application of mentorship program for another aspect of surgical residency training: The importance of academia in surgical training

Traditionally, surgical residency training is more focused on obtaining surgical skills through a well-established coaching system worldwide. However, constant advances in medical science require surgeons to learn not only surgical skills but also the ability of scientific research to improve clinical practice and future professional development. The study aims to emphasize that professional education in terms of scientific research is also significant for surgical residency training.All residents who had been recruited in a medical center for the surgery residency program between years 2006 and 2015 were evaluated in the study. Generally, every resident is assigned to a mentor since the first year of residency. Then, the mentor would help the resident qualify a 2-step evaluation in terms of scientific research during the residency training program.A total of 193 residents were evaluated in the study. All of them had completed the first step regarding oral presentation of their designated research, and the majority of residents obtained 80 to 90 points that were rated by referees. Overall, 102 residents (52.8%) had completed the second step with the publication of a research manuscript. The percentage of residents who had fulfilled the criteria of this 2-step assessment ranged from 35.3% to 81.8% by year.The continuing education for surgical residents should not be limited in coaching clinical practice. Scientific research is also essential for current surgical residency training, and a formal mentorship program may be beneficial for the future professional development of surgical residents. However, the success of the 2-step evaluation could possibly depend on the career choices of the residents instead of the mentorship program.

Can Virtual Reality Be Used to Track Skills Decay During the Research Years?

BACKGROUND

Time away from surgical practice can lead to skills decay. Research residents are thought to be prone to skills decay, given their limited experience and reduced exposure to clinical activities during their research training years. This study takes a cross-sectional approach to assess differences in residents' skills at the beginning and end of their research years using virtual reality. We hypothesized that research residents will have measurable decay in psychomotor skills when evaluated using virtual reality.

METHODS

Surgical residents (n = 28) were divided into two groups; the first group was just beginning their research time (clinical residents: n = 19) and the second group (research residents: n = 9) had just finished at least 2 y of research. All participants were asked to perform a target-tracking task using a haptic device, and their performance was compared using Welch's t-test.

RESULTS

Research residents showed a higher level of "tracking error" (1.69 ± 0.44 cm versus 1.40 ± 0.19 cm; P = 0.04) and a similar level of "path length" (62.5 ± 10.5 cm versus 62.1 ± 5.2 cm; P = 0.92) when compared with clinical residents.

CONCLUSIONS

The increased "tracking error" among residents at the end of their research time suggests fine psychomotor skills decay in residents who spend time away from clinical duties during laboratory time. This decay demonstrates the need for research residents to regularly participate in clinical activities, simulation, or assessments to minimize and monitor skills decay while away from clinical practice. Additional longitudinal studies may help better map learning and decay curves for residents who spend time away from clinical practice.