Laser thermal therapy monitoring using complex differential variance in optical coherence tomography
Bibliographic record
Abstract
Conventional thermal therapy monitoring techniques based on temperature are often invasive, limited by point sampling, and are indirect measures of tissue injury, while techniques such as magnetic resonance and ultrasound thermometry are limited by their spatial resolution. The visualization of the thermal coagulation zone at high spatial resolution is particularly critical to the precise delivery of thermal energy to epithelial lesions. In this work, an integrated thulium laser thermal therapy monitoring system was developed based on complex differential variance (CDV), which enables the 2D visualization of the dynamics of the thermal coagulation process at high spatial and temporal resolution with an optical frequency domain imaging system. With proper calibration to correct for noise, the CDV-based technique was shown to accurately delineate the thermal coagulation zone, which is marked by the transition from high CDV upon heating to a significantly reduced CDV once the tissue is coagulated, in 3 different tissue types ex vivo: skin, retina, and esophagus. The ability to delineate thermal lesions in multiple tissue types at high resolution opens up the possibility of performing microscopic image-guided procedures in a vast array of epithelial applications ranging from dermatology, ophthalmology, to gastroenterology and beyond.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".