P.165 Comparison of preoperative diffusion tensor imaging tractography platforms for intrinsic brain lesions
Bibliographic record
Abstract
Background: Diffusion tensor imaging (DTI) tractography enables detailed visualization of white matter tracts (WMT) relevant to surgical planning. Head-to-head performance of clinically available DTI software has not been assessed. We retrospectively compared Synaptive’s Modus Plan™(vers. 2.0.1.1743) and Medtronic’s StealthViz™(vers. 1.4) software, focusing on workflow, usability, stability, and capacity to generate WMT reconstructions. Methods: Retrospective evaluation of patients (n=13) with intrinsic brain lesions (01/2021-12/2023) with MP and SV software. Corticospinal and optic radiation WMT reconstruction was attempted according to the manufacturers specifications and was rated as clinically useful or not, based on anatomic plausibility. Duration of each analysis step (image importing, post-processing, segmentation, fine-tuning and tract export) was recorded. Ease of use, degree of clinician input, program stability, and tract output type were also assessed. Results: 13 patients (31±;19 yrs; 6F) were included. Mean workflow duration was significantly longer for MP (22:51 min) versus SV (7:35 min) (p<0.0001). Successful WMT reconstruction occurred in 9/13 (69.2%) with MP, versus 5/13 (38.5%) for SV. MP was rated to have superior usability, stability and required less clinician input but technical parameters (e.g. FA) or export object type was inflexible. Conclusions: Synaptive’s MP permitted more robust WMT reconstruction with enhanced usability and stability but with significantly longer workflow.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".