Comparison of Macintosh Laryngoscope, King Vision®, VividTrac®, AirAngel Blade®, and a Custom-Made 3D-Printed Video Laryngoscope for Difficult and Normal Airways in Mannequins by Novices-A Non-Inferiority Trial.

Background: Endotracheal intubation (ETI) is a cornerstone of airway management. The gold standard device for ETI is still the direct laryngoscope (DL). However, video laryngoscopes (VLs) are now also widely available and have several proven advantages. The VL technique has been included in the major airway management guidelines. During the COVID-19 pandemic, supply chain disruption has raised demand for 3D-printed medical equipment, including 3D-printed VLs. However, studies on performance are only sparsely available; thus, we aimed to compare 3D-printed VLs to the DL and other VLs made with conventional manufacturing technology. Methods: Forty-eight medical students were recruited to serve as novice users. Following brief, standardized training, students executed ETI with the DL, the King Vision® (KV), the VividTrac® (VT), the AirAngel Blade® (AAB), and a custom-made 3D-printed VL (3DVL) on the Laerdal® airway management trainer in normal and difficult airway scenarios. We evaluated the time to and proportion of successful intubation, the best view of the glottis, esophageal intubation, dental trauma, and user satisfaction. Results: The KV and VT are proved to be superior (p < 0.05) to the DL in both scenarios. The 3DVL's performance was similar (p > 0.05) or significantly better than that of the DL and mainly non-inferior (p > 0.05) compared to the KV and VT in both scenarios. Regardless of the scenario, the AAB proved to be inferior (p < 0.05) even to the DL in the majority of the variables. The differences between the devices were more pronounced in the difficult airway scenario. The user satisfaction scores were in concordance with the aforementioned performance of the scopes. Conclusions: Based upon our results, we cannot recommend the AAB over the DL, KV, or VT. However, as the 3DVL showed, 3D printing indeed can provide useful or even superior VLs, but prior to clinical use, meticulous evaluation might be recommended.

Validation of a novel, low-fidelity virtual reality simulator and an artificial intelligence assessment approach for peg transfer laparoscopic training.

Simulators are widely used in medical education, but objective and automatic assessment is not feasible with low-fidelity simulators, which can be solved with artificial intelligence (AI) and virtual reality (VR) solutions. The effectiveness of a custom-made VR simulator and an AI-based evaluator of a laparoscopic peg transfer exercise was investigated. Sixty medical students were involved in a single-blinded randomised controlled study to compare the VR simulator with the traditional box trainer. A total of 240 peg transfer exercises from the Fundamentals of Laparoscopic Surgery programme were analysed. The experts and AI-based software used the same criteria for evaluation. The algorithm detected pitfalls and measured exercise duration. Skill improvement showed no significant difference between the VR and control groups. The AI-based evaluator exhibited 95% agreement with the manual assessment. The average difference between the exercise durations measured by the two evaluation methods was 2.61 s. The duration of the algorithmic assessment was 59.47 s faster than the manual assessment. The VR simulator was an effective alternative practice compared with the training box simulator. The AI-based evaluation produced similar results compared with the manual assessment, and it could significantly reduce the evaluation time. AI and VR could improve the effectiveness of basic laparoscopic training.