Neural Network Prediction of Keratoconus in AIPL1-Linked Leber Congenital Amaurosis: A Proof-of-Concept Pilot Study
Bibliographic record
Abstract
Background/Objectives: Keratoconus (KC) can rapidly erode vision in children with Leber congenital amaurosis (LCA), yet screening usually depends on costly corneal imaging that is often unavailable. We evaluated whether a lightweight, image-free neural network fed only routine clinical and genetic variables can detect KC in patients with AIPL1-related LCA. Methods: This retrospective, proof-of-concept pilot study analyzed chart data for 19 children with biallelic AIPL1 mutations (6 with KC) seen at five tertiary eye centers between January and December 2004. Ten baseline predictors were entered into a feed-forward neural network. Records were randomly split 60/20/20 into training, validation and test sets; 20 replicate networks were trained. The mean test accuracy, sensitivity and specificity across runs were the primary outcomes. Results: The ensemble achieved a mean test accuracy of 91.6% (SD 12.8%), sensitivity of 87.5% (SD 13.1%) and specificity of 93.5% (SD 17.0%). A total of 6 of the 20 runs made no test-set errors, and 16 achieved 100% specificity. The median training time per network was less than 1 s on a laptop CPU. Conclusions: This exploratory pilot shows that a point-of-care, image-free neural network using readily available clinical and genetic data accurately identified KC in AIPL1-LCA. External validation in larger, contemporary cohorts is warranted, but the approach could help triage scarce imaging resources and enable timely corneal–collagen cross-linking in settings where tomography is inaccessible.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".