MRI texture heterogeneity in the optic nerve predicts visual recovery after acute optic neuritis
Bibliographic record
Abstract
PURPOSE: To test the feasibility of using multi-scale MRI texture analysis to assess optic nerve pathology and to investigate how visual recovery relates to the severity of acute tissue damage in the optic nerve in patients after optic neuritis (ON). MATERIALS AND METHODS: We recruited 25 patients with acute ON. Retinal nerve fiber layer (RNFL) thickness; MRI lesion length and enhancement; optic nerve area ratio; and multi-scale MRI texture analysis, a measure of structural integrity, were used to assess tissue damage at baseline, and at 6 and 12 months. The recovery in vision was defined as the functional outcome. Eight healthy subjects were imaged for control. RESULTS: We identified 25 lesions in the affected eyes (9 enhanced) and 5 in the clinically non-affected eyes (none enhanced). At baseline, we found that RNFL values were 20% thicker and lesion texture 14% more heterogeneous in the affected eyes than in the non-affected eyes, and lesion texture ratio of affected to non-affected eyes was greater in patients than in controls. In the affected eyes, visual acuity recovered significantly over 6 (18/23 patients) and 12 months (18/21 patients) when RNFL thickness and optic nerve area ratio decreased over time. Texture heterogeneity in the standard MRI of acute optic nerve lesions was the only measure that predicted functional recovery after ON. CONCLUSIONS: Tissue heterogeneity may be a potential measure of functional outcome in ON patients and advanced analysis of the texture in standard MRI could provide insights into mechanisms of injury and recovery in patients with similar disorders.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".