A Diffuse Reflectance Spectroscopy Instrument for use in the Optical Biopsy of Brain Tumour Margins
Bibliographic record
Abstract
Optical biopsy is a medical technique that uses light to perform non-invasive analysis of tissue in-situ. This technology has many applications in the medical profession, opening up exciting new possibilities for surgical guidance and diagnosis of malignancies and other conditions. Optical biopsy allows a medical professional to perform near instantaneous, real time analysis of tissue composition without the need to physically remove tissue from the body, as required in traditional biopsy. A technique frequently used for this purpose is diffuse reflectance spectroscopy (DRS): collection and analysis of the spectrum of light reflected from a material. Another technique frequently used for optical biopsy is laser induced fluorescence spectroscopy (LIFS): analysis of the fluorescence spectrum returned by a material when illuminated at a specific wavelength. This thesis discusses the design and construction of a spatially resolved DRS system intended for use in a dual modality DRS and time resolved LIFS optical biopsy instrument for clinical analysis of brain tissue. This instrument is specifically intended for use in the surgical removal of malignant gliomas: infiltrating tumours associated with a poor patient prognosis. Theoretical simulation based studies were used to optimize the design of a compact, dual modality fibre optic probe for use in the system and a novel algorithm was developed to allow recovery of the optical properties of tissue from reflectance spectra obtained with this probe. This probe was manufactured and a corresponding spectrometer based system was created for the acquisition of diffuse reflectance spectra. Components were designed to allow sterilization and thus clinical use in an operating room environment. A laboratory trial of this system demonstrated its range and ability to recover the optical properties of lipid emulsion optical phantoms.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".