Trends in biothermophotonics and bioacoustophotonics of tissues
Bibliographic record
Abstract
Recent trends in bioacoustophotonics and biothermophotonics of tissues are presented. The presentation is centered on the development of well-known frequency-domain photothermal and photoacoustic techniques to address issues associated with diffuse photon density waves during optical excitation of turbid media, both in hard tissues (teeth) and soft tissues. These methods have concrete advantages over the conventional pulsed-laser counterparts. In Part I we present biothermophotonic principles and applications to the detection of the carious state in human teeth as embodied by laser photothermal radiometry supported by modulated luminescence. The emphasis is on the abilities of these techniques to approach important problems such as the diagnosis of occlusal pits and fissures and interproximal lesions between teeth which normally go undetected by x-ray radiographs. In Part II we present theoretical and experimental results in frequency-domain bioacoustophotonics of turbid media, such as soft tissues, and we describe the development of sensitive sub-surface imaging methodologies which hold the promise for sensitive diagnostics of cancerous lesions in e.g. a human breast. Results using tissue phantoms and ex-vivo specimens are discussed and the current level of subsurface lesion sensitivity compared to state-of-the-art pulsed photoacoustic techniques is examined. In summary, advances in coupled frequency-domain diffuse-photon-density-wave and thermal or thermoelastic responses of turbid media constitute new trends in bioacoustophotonics and biothermophotonics promising for their signal quality and high dynamic range.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".