Assessment of photobleaching during endoscopic autofluorescence imaging of the lower GI tract
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: In autofluorescence endoscopy, the difference in the fluorescence of intrinsic fluorophores is imaged to help visualize pre-malignant lesions, as in the system evaluated here. In this, blue light is used for excitation and the green autofluorescence is normalized by the red diffuse reflectance and presented using a false color scale. The present study was designed to quantify the degree of fluorescence photobleaching induced by the excitation light during use in the colon, since significant photobleaching could lead to false interpretation of the images, particularly false-positive lesions. STUDY DESIGN: Measurements were made ex vivo and in vivo, both using the endoscopic imaging system and a separate fiberoptic spectroscopy probe in externalized rat jejunum and in patients undergoing routine colonoscopy, using exposures typical of autofluorescence endoscopic examination. RESULTS: Photobleaching could be potentially caused at blue light exposure. However, at light intensities and exposure times that are typically used in clinical practice, the average photobleaching (% loss of peak fluorescence intensity) was <1% and <6% in the rat and human tissues, respectively. Nevertheless, the range was large: from -17% to +18% in rats and -33% to +43% in patients, where negative values denote an apparent increase in fluorescence. Both the large positive and negative deviations are believed in part to be due to a measurement artifact caused by uncontrollable tissue motility. SUMMARY AND CONCLUSIONS: It is concluded that, using exposures typically encountered in clinical practice, there is minimal photobleaching during fluorescence endoscopy at exposure such as are used in the Onco-LIFE and comparable systems. The small changes in fluorescence intensity and spectral shift that do occur are not likely to be detectable by eye, and so should not impact significantly on the diagnostic accuracy of the technique.
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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 | 0.000 | 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) | 0.001 | 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".