Sallaberry, Lucas H.
Queiroz, Anna C. M.
Machado, Maria A. A. M.
Crivello Jr, Oswaldo
Total Authors: 7
 Univ Sao Paulo, Polytech Sch, Comp & Digital Syst Engn, Sao Paulo - Brazil
 Univ Sao Paulo, Inst Math & Stat, Dept Stat, Sao Paulo - Brazil
 Univ Sao Paulo, Inst Psychol, Sao Paulo - Brazil
 Stanford Univ, Stanford, CA 94305 - USA
 Univ Sao Paulo, Orthodont & Publ Hlth, Dent Sch, Sao Paulo - Brazil
 Univ Sao Paulo, Culture & Extens, Sao Paulo - Brazil
Total Affiliations: 6
JOURNAL OF DENTAL EDUCATION;
Web of Science Citations:
Objectives Administering anesthesia to the inferior alveolar nerve is 1 of the most stressful processes in dental training. Most studies using virtual reality (VR) for dental training have used non-immersive technologies. The purpose of this work is to assess the impact of immersive technologies on skills training. Methods On May 2019, an experimental study was conducted with 163 clinical dental students, divided into 4 groups across 2 phases (preceptorship and training) with haptic feedback either On or Off. The participants trained on the inferior alveolar dental anesthesia procedure in a haptic VR simulator. Their technical skills were evaluated in terms of needle insertion features which were computed from a haptic device providing kinematic data. Also, the participants reported their subjective experience with syringe handling and simulator sickness. A machine learning method was implemented to automatically evaluate the needle insertion point performance of the student. Results Groups receiving immersive preceptorship and/or immersive training showed more accuracy and confidence in administering the anesthesia. Participants perceived a high sense of realism with the haptic feedback when handling the syringe. The machine learning method was validated, with an accuracy of 84%, as a good classifier to assess a student's needle insertion point performance. Conclusions The immersive VR simulator allows the practice of the inferior alveolar nerve block under near real conditions and with immediate feedback to the dental student with respect to the needle insertion point. This machine learning based automatic evaluation provides a method to improve technical skills, contributing to dental training. (AU)