Automatic detection of subtle focal cortical dysplasia using surface-based features on MRI
Bibliographic record
Abstract
Focal cortical dysplasia (FCD) is an important cause of pharmacoresistant epilepsy. Small FCD lesions are difficult to distinguish from non-lesional cortex and remain often overlooked on radiological MRI inspection. This paper presents a method to detect small FCD lesions on Tl-MRI relying on surface-based features: cortical thickness, gradient magnitude at the white-matter / grey-matter interface, cortical signal intensity, curvature and depth of inner-cortical surface. These features best describe the visual and morphometric characteristics of small FCD, and allow differentiating it from healthy tissues. The automatic detection was performed by a neural-network bagging trained on manual labels. The method was tested on 19 patients with small FCD and identified the lesion in 89% (17/19) of cases. Cluster analysis demonstrated that the lesional cluster was the largest in 76% (13/17) of identified cases. This new approach may assist the presurgical evaluation of patients with intractable epilepsy, especially those with "MRI-negative" epilepsy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".