Robot-Assisted Insular Depth Electrode Implantation Through Oblique Trajectories: 3-Dimensional Anatomical Nuances, Technique, Accuracy, and Safety
Bibliographic record
Abstract
BACKGROUND: The insula is a deep cortical structure that has renewed interest in epilepsy investigation. Invasive EEG recordings of this region have been challenging. Robot-assisted stereotactic electroencephalography has improved feasibility and safety of such procedures. OBJECTIVE: To describe technical nuances of three-dimensional (3D) oblique trajectories for insular robot-assisted depth electrode implantation. METHODS: Fifty patients who underwent robot-assisted depth electrode implantation between June 2017 and December 2018 were retrospectively analyzed. Insular electrodes were implanted through oblique, orthogonal, or parasagittal trajectories. Type of trajectories, accuracy, number of contacts within insular cortex, imaging, and complication rates were analyzed. Cadaveric and computerized tomography/magnetic resonance imaging 3D reconstructions were used to visualize insular anatomy and the technical implications of oblique trajectories. RESULTS: Forty-one patients (98 insular electrodes) were included. Thirty (73.2%) patients had unilateral insular coverage. Average insular electrodes per patient was 2.4. The mean number of contacts was 7.1 (SD ± 2.91) for all trajectories and 8.3 (SD ± 1.51) for oblique insular trajectories. The most frequently used was the oblique trajectory (85 electrodes). Mean entry point error was 1.5 mm (0.2-2.8) and target error was 2.4 mm (0.8-4.0), 2.0 mm (1.1-2.9) for anterior oblique and 2.8 mm (0.8-4.9) for posterior oblique trajectories. There were no complications related to insular electrodes. CONCLUSION: Oblique trajectories are the preferred method for insular investigation at our institution, maximizing the number of contacts within insular cortex without traversing through sulci or major CSF fissures. Robot-assisted procedures are safe and efficient. 3D understanding of the insula's unique anatomical features can help the surgeon to improve targeting of this structure.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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".