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Record W3206003967 · doi:10.2196/30653

Mixed Reality and Haptic–Based Dental Simulator for Tooth Preparation: Research, Development, and Preliminary Evaluation

2021· article· en· W3206003967 on OpenAlex
Yaning Li, Hongqiang Ye, Siyu Wu, Xiaohan Zhao, Yunsong Liu, Longwei Lv, Ping Zhang, Xiao Zhang, Yongsheng Zhou

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJMIR Serious Games · 2021
Typearticle
Languageen
FieldDentistry
TopicDental Research and COVID-19
Canadian institutionsnot available
FundersPeking University
KeywordsVirtual realityHaptic technologySimulationDental educationComputer scienceDentistryMedicineHuman–computer interaction

Abstract

fetched live from OpenAlex

BACKGROUND: Virtual reality (VR) dental simulators are currently used in preclinical skills training. However, with the development of extended reality technologies, the use of mixed reality (MR) has shown significant advantages over VR. OBJECTIVE: This study aimed to describe the research and development of a newly developed MR and haptic-based dental simulator for tooth preparation and to conduct a preliminary evaluation of its face validity. METHODS: A prototype of the MR dental simulator for tooth preparation was developed by integrating a head-mounted display (HMD), special force feedback handles, a foot pedal, computer hardware, and software program. We recruited 34 participants and divided them into the Novice group (n=17) and Skilled group (n=17) based on their clinical experience. All participants prepared a maxillary right central incisor for an all-ceramic crown in the dental simulator, completed a questionnaire afterward about their simulation experience, and evaluated hardware and software aspects of the dental simulator. RESULTS: Of the participants, 74% (25/34) were satisfied with the overall experience of using the Unidental MR Simulator. Approximately 90% (31/34, 91%) agreed that it could stimulate their interest in learning, and 82% (28/34) were willing to use it for skills training in the future. Differences between the 2 study groups in their experience with the HMD (resolution: P=.95; wearing comfort: P=.10), dental instruments (P=.95), force feedback of the tooth (P=.08), simulation of the tooth preparation process (P=.79), overall experience with the simulation (P=.47), and attitude toward the simulator (improves skills: P=.47; suitable for learning: P=.36; willing to use: P=.89; inspiring for learning: P=.63) were not significant. The Novice group was more satisfied with the simulator's ease of use (P=.04). There were significant positive correlations between the overall experience with the simulation and the HMD's resolution (P=.03) and simulation of the preparation process (P=.001). CONCLUSIONS: The newly developed Unidental MR Simulator for tooth preparation has good face validity. It can achieve a higher degree of resemblance to the real clinical treatment environment by improving the positional adjustment of the simulated patients, for a better training experience in dental skills.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.662
Threshold uncertainty score0.607

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.130
GPT teacher head0.451
Teacher spread0.321 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it