Accuracy of the Intraoral Scanner for Detection of Tooth Wear
Bibliographic record
Abstract
OBJECTIVE: The aim of this work was to study the accuracy of the intraoral scanner for detection of tooth wear in natural teeth by using micro-computed tomography (micro-CT) as a gold standard. MATERIALS AND METHODS: Twenty premolars were prepared, fixed in acrylic blocks, and scanned with an intraoral scanner (iTero Element® 2) and micro-CT for baseline reference images before artificial tooth wear induction. The samples were then scrubbed with abrasive sandpaper 20 times and scanned with the intraoral scanner. They were then superimposed with the reference images utilising the "TimeLapse" feature of the scanner until the abraded area appeared yellow, indicating tooth surface loss in the 50-200 μm range. The same samples were then rescanned by micro-CT to measure the actual tooth surface loss. This procedure was repeated for the subsequent experimental tooth surface loss of 200-400 μm range (orange areas) and 400-750 μm range (red areas). The collected data were analysed for sensitivity, positive predictive value (PPV), and accuracy. Level of statistical significance was set at .05. RESULTS: In the detection of experimental tooth surface loss, the specificity, PPV, and accuracy of the intraoral scanner were 98%, 98%, and 97%, respectively. CONCLUSIONS: The iTero® intraoral scanner can be recommended to be a suitable screening tool for tooth wear in routine dental practice.
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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 | 0.003 | 0.011 |
| 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) | 0.001 | 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".