A Virtual 3D Dynamic Model of Caries Lesion Progression as a Learning Object for Caries Detection Training and Teaching: Video Development Study
Bibliographic record
Abstract
BACKGROUND: In the last decade, 3D virtual models have been used for educational purposes in the health sciences, specifically for teaching human anatomy and pathology. These models provide an opportunity to didactically visualize key spatial relations that can be poorly understood when taught by traditional educational approaches. Caries lesion detection is a crucial process in dentistry that has been reported to be difficult to learn. One especially difficult aspect is linking clinical characteristics of the different severity stages with their histological features, which is fundamental for treatment decision-making. OBJECTIVE: This project was designed to develop a virtual 3D digital model of caries lesion formation and progression to aid the detection of lesions at different severity stages as a potential complement to traditional lectures. METHODS: Pedagogical planning, including identification of objectives, exploration of the degree of difficulty of caries diagnosis-associated topics perceived by dental students and lecturers, review of the literature regarding key concepts, and consultation of experts, was performed prior to constructing the model. An educational script strategy was created based on the topics to be addressed (dental tissues, biofilm stagnation areas, the demineralization process, caries lesion progression on occlusal surfaces, clinical characteristics related to different stages of caries progression, and histological correlations). Virtual 3D models were developed using the Virtual Man Project and refined using multiple 3D software applications. In the next phase, computer graphic modelling and previsualization were executed. After that, the video was revised and edited based on suggestions. Finally, explanatory subtitles were generated, the models were textured and rendered, and voiceovers in 3 languages were implemented. RESULTS: We developed a 6-minute virtual 3D dynamic video in 3 languages (English, Spanish, and Brazilian Portuguese) intended for dentists and dental students to support teaching and learning of caries lesion detection. The videos were made available on YouTube; to date, they have received more than 100,000 views. CONCLUSIONS: Complementary pedagogical tools are valuable to support cariology education. This tool will be further tested in terms of utility and usability as well as user satisfaction in achieving the proposed objectives in specific contexts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".