RAMAN SPECTROSCOPY FOR <i>IN VIVO</i> TISSUE ANALYSIS AND DIAGNOSIS, FROM INSTRUMENT DEVELOPMENT TO CLINICAL APPLICATIONS
Bibliographic record
Abstract
Raman spectroscopy is a noninvasive, nondestructive analytical method capable of determining the biochemical constituents based on molecular vibrations. It does not require sample preparation or pretreatment. However, the use of Raman spectroscopy for in vivo clinical applications will depend on the feasibility of measuring Raman spectra in a relatively short time period (a few seconds). In this work, a fast dispersive-type near-infrared (NIR) Raman spectroscopy system and a skin Raman probe were developed to facilitate real-time, noninvasive, in vivo human skin measurements. Spectrograph image aberration was corrected by a parabolic-line fiber array, permitting complete CCD vertical binning, thereby yielding a 16-fold improvement in signal-to-noise ratio. Good quality in vivo skin NIR Raman spectra free of interference from fiber fluorescence and silica Raman scattering can be acquired within one second, which greatly facilitates practical noninvasive tissue characterization and clinical diagnosis. Currently, we are conducting a large clinical study of various skin diseases in order to develop Raman spectroscopy into a useful tool for non-invasive skin cancer detection. Intermediate data analysis results are presented. Recently, we have also successfully developed a technically more challenging endoscopic Laser-Raman probe for early lung cancer detection. Preliminary in vivo results from endoscopic lung Raman measurements are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".