The detection of glaucoma using an artificial neural network
Bibliographic record
Abstract
The scanning laser ophthalmoscope is a device used by ophthalmologists to obtain topographic images of patients' optic nerve heads (ONHs). Measurements are taken from these images that quantitatively describe the shape of the ONH. Glaucoma involves the loss of retinal nerve fibers, which in turn produces a change in the ONH shape. However, it is not known which shape parameters are most relevant to the diagnosis of glaucoma. To solve this problem, a feedforward artificial neural network (ANN) was designed to discriminate between patient data. Patients were first independently classified using perimetry data (visual fields) into normal and abnormal (glaucomatous) groups. The ANN was trained using error backpropagation (n=89 samples) and the classification model was cross validated using one normal, and one abnormal sample. The entire data set (45 normals and 46 abnormals) was utilized for cross validation and each time the error rate of the training set was required to be less than 15%. The ANN gave an overall classification rate of 86.7%, with a specificity (correct normals) of 88.9% and a sensitivity (correct abnormals) of 84.4%. The ANN classification model, with only two hidden units, generalized well which indicates that the ONH measurements are useful for the detection of glaucoma.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".