Intraoperative In-vivo Verification of Tissues by Optical Spectroscopy
Bibliographic record
Abstract
Any surgical intervention, even a minimal one, poses a potential danger, which is not always predictable.At laparoscopic and robot-assisted surgery, which are intensively developed today, the surgeon is almost devoid of tactile sensations.In addition, a colour reproduction of the image may be slightly altered.It leads to interpretive errors [1].Another problem exists in regenerative medicine.At local transplantations of stem cells, the cells will differentiate into the tissue, they are injected.Thus, the necessity to create a convenient tool that allows the surgeon to perform an intraoperative rapid identification and verification of anatomical structures and types of tissues without its damaging is apparent.Non-invasive and minimally invasive optical diagnostic techniques, in particular, in-vivo spectrophotometric methods, are known in medicine for a long time, about a quarter of a century, or more [2].In recent years, one can see many attempts to use them as intraoperative diagnostic tools for navigation and tissue identification at surgery interventions [1,3].There are many studies devoted to the analysis of diffuse backscattering spectra for various tissues to find specific quantitative criteria for their differentiation [3].There are also many researches attempting to verify tissues using fluorescence spectra [4] and autofluorescence ones [5,6].In our study, we used the combined diffuse reflectance spectroscopy and fluorescent spectroscopy technique, which exploits the differences in absorption, scattering and fluorescent properties of biological tissues.The study was carried out with the use of our new laser diagnostic system "Multicom", developed by LLC "Research and Development Center EOS-Medica".Optical properties of various types of tissues of laboratory white rats were measured.To reduce a number of variables, the principal component analysis with rotation (varimax) was applied.For the fluorescence spectrum, 98.3% of the dispersion was explained by five principal components.For the spectrum of the intensity of the diffuse backscattering, six principal components allowed to explain 92.2% of the dispersion.The eleven principal components obtained were included in the discriminant analysis.Statistical processing of data was carried out by IBM SPSS Statistics v23 (IBM corp., USA).Eight out of eleven types of tissues were verified with the accuracy more than 80%.Thus, we showed experimentally that the combined spectroscopy technique may be useful for intraoperative navigation and identification of types of tissues.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".