Automated analysis of normal and glaucomatous optic nerve head topography images.
Bibliographic record
Abstract
PURPOSE: To classify images of optic nerve head (ONH) topography obtained by scanning laser ophthalmoscopy as normal or glaucomatous without prior manual outlining of the optic disc. METHODS: The shape of the ONH was modeled by a smooth two-dimensional surface with a shape described by 10 free parameters. Parameters were adjusted by least-squares fitting to give the best fit of the model to the image. These parameters, plus others derived from the image using the model as a basis, were used to discriminate between normal and abnormal images. The method was tested by applying it to ONH topography images, obtained with the Heidelberg Retina Tomograph, from 100 normal volunteers and 100 patients with glaucomatous visual field damage. RESULTS: Many of the parameters derived from the fits differed significantly between normal and glaucomatous ONH images. They included the degree of surface curvature of the disc region surrounding the cup, the steepness of the cup walls, the goodness-of-fit of the model to the image in the cup region, and measures of cup width and cup depth. The statistics of the parameters were analyzed and were used to construct a classifier that gave the probability, P(G), that each image came from the glaucoma population. Images were classified as abnormal if P(G) > 0.5. The probabilities assigned to each image were in most cases close to 0 (normal) or 1 (abnormal). Eighty-seven percent of the sample was confidently classified with P(G) < 0.3 or P(G) > 0.7. Within this group, the overall classification accuracy was 92%. The overall accuracy of the method (the mean of sensitivity and specificity, which were similar) in the whole sample was 89%. CONCLUSIONS: ONH images can be classified objectively and dependably by an automated procedure that does not require prior manual outlining of disc boundaries.
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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 | 0.002 | 0.000 |
| 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".