Impact of immersion on the performance of haptic virtual reality for inferior alveolar nerve anesthesia training. - Collaço, Elen (2019)
The procedure for the administration of anesthesia to the inferior alveolar nerve is one of the most stressful in dental training. Although most studies have made efforts to develop Virtual Reality (VR) dental training, the majority have used non-immersive technologies. However, the effects of immersion, in both the explanation and training phases, on the performance of the dental anesthesia process, remains unclear. Therefore, in this work, we conducted an experimental study to evaluate the impact of immersion on the performance of haptic VR for inferior alveolar nerve anesthesia training. The immersive haptic VR, named VIDA Odonto R, was developed in our research group, and this work contributed with that development. The experiment involved 163 clinical students, divided into 4 groups under different combinations of immersion and haptic feedback. Their performance was evaluated in terms of execution time and the measurements related to the needle insertion. Moreover, we developed and tested a machine learning method for automatic evaluation of the dentistry student\'s performance, that is an innovative methodology for needle insertion assessment in VR-based dental training. Also, the participants were asked to report their syringe handling, simulator sickness and embodiment experience through questionnaires. Results indicated that groups receiving immersive explanations and/or immersive training showed more accuracy and confidence in administering the anesthesia. Participants perceived a high sense of the virtual hand ownership and realism of the haptic feedback when handling the syringe. Moreover, the proposed experimental design can be applied to any other HMD-based VR simulators that involves performance analysis. These results bring us one step closer to understanding the impact of head-mounted displays (HMDs) on virtual anesthesia training before their adoption at scale, and provide insights in how needle insertion performance can be assessed automatically by machine learning.