Human Brain Tissue Identification Using Coherent Anti-Stokes Raman Scattering Spectroscopy and Diffuse Reflectance Spectroscopy for Deep Brain Stimulation Surgery
Bibliographic record
Abstract
Abstract We assess the feasibility of using diffuse reflectance spectroscopy (DRS) and coherent anti-Stokes Raman scattering spectroscopy (CARS) as optical tools for human brain tissue identification during deep brain stimulation (DBS) electrode insertion, thereby providing a promising avenue for additional real-time neurosurgical guidance. DRS and CARS spectra were acquired using a custom-built optical probe integrated in a commercial DBS lead. The electrode was inserted to target 3 specific regions in each of the brain hemispheres of a human cadaver, for a total of 6 insertions. DRS and CARS spectra were acquired during the lead insertion at constant position increments. Spectra were analyzed using a dimensionality-reduction technique, principal component analysis (PCA), and the k-means clustering method to classify each spectrum as being either white matter or gray matter for each trajectory. The results were compared to visual tissue classifications performed on histological brain sections and classifications based on a preoperative magnetic resonance imaging (MRI) scan. DRS and CARS spectra obtained using the optical probe can identify white and gray matter during DBS lead insertion. In addition, the tissue composition along the trajectory toward a specific target is unique and can be differentiated by the optical probe. Moreover, the results obtained with PCA results suggest that DRS might be able to detect the presence of blood in front of the optical probe due to the strong optical absorption of hemoglobin in this wavelength range. It is possible to use optical measurements from the DBS lead during surgery to identify white and gray matter, and possibly the presence of blood in human brain tissue. This information could provide better guidance in real time and prevent hemorrhages without disrupting the normal surgical workflow. Further developments are required to fully integrate these tools into standard clinical procedures.
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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".