Improvements to a laser Raman spectroscopy system for reducing the false positives of autofluorescence bronchoscopies
Bibliographic record
Abstract
Preneoplastic lesions of the bronchial tree have a high probability of developing into malignant tumours. Currently the best method for localizing them for further treatment is a combined white light and autofluorescence bronchoscopy (WLB+AFB). Unfortunately the average specificity from large clinical trials for this combined detection method is low at around 60%, which can result in many false positives. However a recent pilot study showed that adding a point laser Raman spectroscopy (LRS) measurement improved the specificity of detecting lesions with high grade dysplasia or carcinoma in situ to 91% with a sensitivity of 96% compared to WLB+AFB alone. Despite this success, there is still room for much improvement. One constant need is to find better ways to measure the inherently weak Raman emissions in vivo which will result in even better diagnostic sensitivity and specificity. With this aim in mind a new generation Raman system was developed. The system uses the latest charge coupled device (CCD) with low noise, and fast cool down times. A spectrometer was incorporated that was able to measure both the low and high frequency Raman emissions with high resolution. The Raman catheter was also redesigned to include a visible light channel to facilitate the accurate indication of the area being measured. Here the benefits in the adjunct use of LRS to WLB + AFB are presented, and description of the new system and the improvements it offers over the old system are shown.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".