Side‐Viewing Endoscopic Raman Spectroscopy for Angle‐Resolved Analysis of Luminal Organs
Bibliographic record
Abstract
Abstract There are many medical applications requiring angle‐resolved analysis of luminal organs. One example is lung cancer detection. Early diagnosis is the most effective way to tackle this disease. This, however, remains a challenge due to the lack of accurate detection technology. The problem is exacerbated in cases of peripheral lung cancer growing on narrow bronchi that are difficult to probe. Here, an endoscopic Raman spectroscopy device is demonstrated with a side‐viewing functionality that enables circumferential scanning spectral measurements inside thin conduits. A tubular micro rotary stepping actuator is custom‐designed and integrated with a Raman probe, for the first time, to scan a probing laser beam sideways for angle‐resolved local Raman analysis with no aid of tissue labeling, toward enabling detection of lesion‐induced biochemical changes in vivo and in real time. A microfabricated prototype is evaluated using test chemicals, harvested animal lung tissue ex vivo, as well as a murine colon model in situ and human skin in vivo. All the test results show excellent agreements with reported reference data while revealing >99% wavenumber accuracies. The study indicates that microactuator‐assisted endoscopic Raman spectroscopy is a promising technology for luminal tissue analysis and encourages further studies of its sensitivity in lesion detection.
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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 | 0.000 |
| 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".