Thermophotonic lock-in imaging: An active thermography system for detecting early carious lesions in human teeth
Bibliographic record
Abstract
Lock-in thermography is an active thermographic method that incorporates quadrature demodulation to retrieve the amplitude and phase of the thermal waves generated inside the sample either optically, acoustically or mechanically. The role of subsurface defects, in this case, is then to shift the thermal-wave centroid and therefore produce dynamic contrast, both in amplitude and phase images, with respect to the intact areas. Thanks to recent advances in infrared camera technology, lock-in thermography has been successfully applied to various industrial fields as a powerful non-destructive evaluation technique but less work has been carried out in medical applications of this technology. The case of biological samples is challenging as these samples are usually translucent and do not effectively absorb the applied optical excitation. Even if they do, the medical codes prevent researchers from applying high power excitation to these samples. As a result, the photothermal signals obtained from biological samples are generally poor in terms of signal-to-noise ratio and this makes signal enhancement methods an inevitable part of lock-in thermography systems used in the medical field. The other significant difference of biological samples is that due to their translucency the infrared radiation emanating from them is governed by a coupled diffuse-photon-density and thermal-wave field, as opposed to purely thermal-wave field in opaque samples, which makes the interpretation of the results even more complicated.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".