Comparison of Long-Wave and Mid-Wave Infrared Imaging Modalities for Photothermal Coherence Tomography of Human Teeth
Bibliographic record
Abstract
The ability to detect dental caries at early stages lies at the heart of minimal intervention dentistry, enabling the curing or arresting of carious lesions before they advance to the cavity stage. Enhanced truncated-correlation photothermal coherence tomography (eTC-PCT) using mid-wave infrared (MWIR) cameras has recently been shown to offer tomographic visualization of early caries. The tomographic slicing ability of such systems, however, is believed to be limited by direct radiative thermal emission through the translucent dental enamel in the 3-5 µm MWIR spectral range. Such radiative emissions can dominate the delayed conductive thermal contributions needed for tomographic reconstruction of internal dental defects. It has been hypothesized that long-wave infrared (LWIR) eTC-PCT systems may offer better tomographic performance by taking advantage of the intrinsic attenuation of direct radiative emission by dental enamel in the LWIR spectral range, enabling more effective delayed conductive thermal contributions from subsurface caries. More than an order of magnitude lower cost of the system is another key attribute of LWIR eTC-PCT which can open the door for downstream translation of the technology to clinics. In this report, we offer a systematic comparison of the performance/effectiveness of caries detection with LWIR and MWIR eTC-PCT systems for detecting natural caries, bacterial caries, and artificially demineralized enamel surfaces. Our results suggest that the low-cost LWIR based eTC-PCT system provides 3D visualization and 2D slice-by-slice images of early caries and internal micro-cracks similar to those obtained from the more expensive MWIR-based eTC-PCT system, albeit with ∼1.3dB lower signal-to-noise ratio.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".