Detecting cracks in teeth and monitoring structural integrity over time with non-invasive PTR-LUM technology a solution for a major clinical challenge
Bibliographic record
Abstract
Detecting cracks in teeth is a long-standing clinical challenge. Patients may complain of diffuse pain on chewing, pain, at times, on temperature change and pain that occurs episodically. Common diagnostic tools such as radiographs and visual examination may not detect cracks. This clinical case study shows how photothermal radiometry and luminescence (PTR-LUM), technology behind the Canary Dental Caries Detection System can detect and monitor cracks clinically as well as quantify the extent of crack. This important clinical feature is not yet available with other caries detection clinical devices. In this clinical situation, the cracks involved a large part of the mesial and distal of a mandibular second molar and the adjacent first molar. It led to a diagnosis of parafunction and placement of a mandibular flat plane bite splint along with the placement of composite restorations to restore the fractures. The science behind the point scan lock-in signal processing results of PTR-LUM technology implemented in The Canary System to clinically detect visible cracks or cracks beneath the enamel surface as well as caries on all tooth surfaces and around restorations is discussed. Amplitude and phase results from PTR-LUM point scans are incorporated into a Canary number output developed for oral health providers and are disclosed for the first time in detail with clinical evidence.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".