Visualization and navigation system development and application for stereotactic deep-brain neurosurgeries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We present the development of a visualization and navigation system and its application in pre-operative planning and intra-operative guidance of stereotactic deep-brain neurosurgical procedures for the treatment of Parkinson's disease, chronic pain, and essential tremor. This system incorporates a variety of standardized functional and anatomical information, and is capable of non-rigid registration, interactive manipulation, and processing of clinical image data. The integration of a digitized and segmented brain atlas, an electrophysiological database, and collections of final surgical targets from previous patients facilitates the delineation of surgical targets and surrounding structures, as well as functional borders. We conducted studies to compare the surgical target locations identified by an experienced stereotactic neurosurgeon using multiple electrophysiological exploratory trajectories with those located by a non-expert using this system on 70 thalamotomy, pallidotomy, thalamic deep-brain stimulation (DBS), and subthalamic nucleus (STN) DBS procedures. The average displacement between the surgical target locations in both groups was 1.95 ± 0.86 mm, 1.83 ± 1.07 mm, 1.88 ± 0.89 mm and 1.61 ± 0.67 mm for each category of surgeries, respectively, indicating the potential value of our system in stereotactic deep-brain neurosurgical procedures, and demonstrating its capability for accurate surgical target initiation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.000 | 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 it