Virtual reality operating room with AI guidance: design and validation of a fire scenario.

BACKGROUND: Operating room (OR) fires are uncommon but disastrous events. Inappropriate handling of OR fires can result in injuries, even death. Aiming to simulate OR fire emergencies and effectively train clinicians to react appropriately, we have developed an artificial intelligence (AI)-based OR fire virtual trainer based on the principle of the "fire triangle" and SAGES FUSE curriculum. The simulator can predict the user's actions in the virtual OR and provide them with timely feedback to assist with training. We conducted a study investigating the validity of the AI-assisted OR fire trainer at the 2019 SAGES Learning Center. METHODS: Fifty-three participants with varying medical experience were voluntarily recruited to participate in our Institutional Review Board approved study. All participants were asked to contain a fire within the virtual OR. Participants were then asked to fill out a 7-point Likert questionnaire consisting of ten questions regarding the face validation of the AI-assisted OR fire simulator. Shapiro-Wilk tests were conducted to test normality of the scores for each trial. A Friedman's ANOVA with post hoc tests was used to evaluate the effect of multiple trials on performance. RESULTS: On a 7-point scale, eight of the ten questions were rated a mean of 6 or greater (72.73%), especially those relating to the usefulness of the simulator for OR fire-containing training. 79.25% of the participants rated the degree of usefulness of AI guidance over 6 out of 7. The performance of individuals improved significantly over the five trials, χ2(4) = 119.89, p < .001, and there was a significant linear trend of performance r = .97, p = 0.006. A pairwise analysis showed that only after the introduction of AI did performance improve significantly. CONCLUSIONS: The AI-guided OR fire trainer offers the potential to assess OR personnel and teach the proper response to an iatrogenic fire scenario in a safe, repeatable, immersive environment.

Surgeons With Five or More Actual Cricothyrotomies Perform Significantly Better on a Virtual Reality Simulator.

BACKGROUND: Discriminating performance of learners with varying experience is essential to developing and validating a surgical simulator. For rare and emergent procedures such as cricothyrotomy (CCT), the criteria to establish such groups are unclear. This study is to investigate the impact of surgeons' actual CCT experience on their virtual reality simulator performance and to determine the minimum number of actual CCTs that significantly discriminates simulator scores. Our hypothesis is that surgeons who performed more actual CCT cases would perform better on a virtual reality CCT simulator. METHODS: 47 clinicians were recruited to participate in this study at the 2018 annual conference of the Society of American Gastrointestinal and Endoscopic Surgeons. We established groups based on three different experience thresholds, that is, the minimal number of CCT cases performed (1, 5, and 10), and compared simulator performance between these groups. RESULTS: Participants who had performed more clinical cases manifested higher mean scores in completing CCT simulation tasks, and those reporting at least 5 actual CCTs had significantly higher (P = 0.014) simulator scores than those who had performed fewer cases. Another interesting finding was that classifying participants based on experience level, that is, attendings, fellows, and residents, did not yield statistically significant differences in skills related to CCT. CONCLUSIONS: The simulator was sensitive to prior experience at a threshold of 5 actual CCTs performed.

Divided Voxels: An efficient algorithm for interactive cutting of deformable objects.

Efficient algorithms that support dynamic topological updates are necessary for the simulation of progressive interactive cutting of deformable objects. Existing mesh-based techniques suffer from the generation of ill-shaped elements whereas voxel grid-based methods require additional cut surfaces to be generated or the use of look-up tables for pre-computed cutting patterns. To overcome these limitations of existing methods, we propose a novel voxel-based topological operator, divide, which divides a voxel into two voxels identical to the original voxel's size by dynamically distributing its voxel elements (nodes, edges) into the newly divided voxels until the cutting of the original voxel is completed. The connectivity between the divided voxels and the neighbors of the original voxel is retained during the cut, and new connectivity between the adjacent divided voxels is generated to represent the continuity of the cut. As a result, the cut surface can be generated directly from the divided voxels on-the-fly, and the correspondence between the cut surface and the simulation voxels is maintained without any additional effort. We use several example problems to demonstrate the efficiency of our method and compare it with other existing approaches.

Disruptions to shared mental models from poor quality of service in collaborative virtual environments.

Collaborative virtual environments are being used in various applications ranging from online games to complex team training scenarios. The key to the success of such environments is the ability of the participants to form a shared mental model of the collaborative task being performed. Poor quality of service can deteriorate user performance and quality of experience, leading to a disruption of this mental model. While the effects of quality of service have been analyzed for traditional desktop environments, these effects remain unclear in collaborative virtual environments during user-to-user interactions. Here, we analyze the role of latency and packet bursts, two common problems in collaborative applications, on both simulator perception and actual task performance in a collaborative fire-fighting simulator. This exploratory study indicates that large latencies have a significant (p < 0.05) impact on the quality of experience, but not task performance. In contrast, packet bursts have a much larger impact on both the quality of experience and performance. Additionally, the network role, such as whether a user is a client or server, showed a significant (p < 0.05) impact on task performance in conditions impaired by packet bursts.