In vitro Detection of Occlusal Caries on Permanent Teeth by a Visual, Light-Induced Fluorescence and Photothermal Radiometry and Modulated Luminescence Methods
Bibliographic record
Abstract
The paradigm shift towards the non-surgical management of dental caries relies on the early detection of the disease. Detection of caries at an early stage is of unequivocal importance for early preventive intervention. OBJECTIVE: The aim of this in vitro study is to evaluate the performance of a visual examination using the International Caries Detection and Assessment System criteria (ICDAS), two quantitative light-induced fluorescence systems (QLF); Inspektor™ Pro and QLF-D Biluminator™ 2 (Inspektor Research Systems B.V., Amsterdam, The Netherlands) and a Photothermal Radiometry and Modulated Luminescence (PTR/LUM), The Canary System® (Quantum Dental Technologies, Toronto, Canada) on detection of primary occlusal caries on permanent teeth. METHODS: 60 teeth with occlusal surface sites ranging from sound to non-cavitated occlusal lesions ICDAS (0-4) were assessed with each detection method twice in a random order. Histological validation was used to compare methods for sensitivity, specificity, % correct and the area under receiver operating characteristic curve (AUC), at standard and optimum sound thresholds. Inter-examiner agreement and intra-examiner repeatability were measured using intraclass correlation coefficient (ICC). RESULTS: Inter-examiner agreement ranged between 0.48 (The Canary System®) and 0.96 (QLF-D Biluminator™2). Intra-examiner repeatability ranged 0.33-0.63 (The Canary System®) and 0.96-0.99 (QLF-D Biluminator™2). Sensitivity ranged 0.75-.096 while specificity ranged 0.43-0.89. AUC was 0.79 (The Canary System®); 0.87 (ICDAS); 0.90 63 (Inspektor™ Pro); and 0.94 (QLF-D Biluminator™2). CONCLUSION: ICDAS had the best combination of sensitivity and specificity followed by QLF-D Biluminator™ 2 at optimum threshold.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".