Deep Convolutional Networks for Automated Detection of Epileptogenic Brain Malformations
Bibliographic record
Abstract
Focal cortical dysplasia (FCD) is a prevalent surgically-amenable epileptogenic malformation of cortical development. On MRI, FCD typically presents with cortical thickening, hyperintensity, and blurring of the gray-white matter interface. These changes may be visible to the naked eye, or subtle and be easily overlooked. Despite advances in MRI analytics, current surface-based algorithms fail to detect FCD in 50% of cases. Moreover, arduous data pre-processing and specialized expertise preclude widespread use. Here we propose a novel algorithm that harnesses feature-learning capability of convolutional neural networks (CNNs) with minimal data pre-processing. Our classifier, trained on a patch-based augmented dataset derived from patients with histologically-validated FCD operates directly on MRI voxels to distinguish the lesion from healthy tissue. The algorithm was trained and cross-validated on multimodal MRI data from a single site (S1) and evaluated on independent data from S1 and six other sites worldwide (S2–S7; 3 scanner manufacturers and 2 field strengths) for a total of 107 subjects. The classifier showed excellent sensitivity (S1: 87%, 35/40 lesions detected; S2–S7: 91%, 61/67 lesions detected) and specificity (S1: 95%, no findings in 36/38 healthy controls; 90%, no findings in 57/63 disease controls). Easy implementation, minimal pre-processing, high performance and generalizability make this classifier an ideal platform for large-scale clinical use, particularly in “MRI-negative” FCD.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".