Advanced MRI analysis methods for detection of focal cortical dysplasia
Bibliographic record
Abstract
In many patients, lesions of focal cortical dysplasia (FCD) may go unrecognized by standard radiological analysis. This is due to the fact that identification of many of these malformations on visual inspection of conventional MRI is difficult due to their subtlety and the complexity of the brain's convolutions. Quantitative MR image processing methods have the potential to help identify lesions that may be overlooked by conventional radiological evaluation. To increase the sensitivity of MRI for the detection of subtle lesions of FCD, we recently developed voxel-based image post-processing methods, including first-order texture analysis and morphological processing modeled on known MRI features of FCD. Using these methods we were able to increase the sensitivity over conventional MRI analysis by more than 30%, while maintaining a high degree of reliability. The image processing methods we developed improve visual detection of FCD, even in cases where no lesion is obvious on MRI. Therefore, these techniques could allow a more precise evaluation of patients with partial epilepsy who could benefit from surgery.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".