Automated detection of diabetic retinopathy in digital retinal images: a tool for diabetic retinopathy screening
Bibliographic record
Abstract
AIMS: To develop a system to detect automatically features of diabetic retinopathy in colour digital retinal images and to evaluate its potential in diabetic retinopathy screening. METHODS: Macular centred 45 degrees colour retinal images from 1273 patients in an inner city diabetic retinopathy screening programme. A system was used involving pre-processing to standardize colour and enhance contrast, segmentation to reveal possible lesions and classification of lesions using an artificial neural network. The system was trained using a subset of images from 500 patients and evaluated by comparing its performance with a human grader on a test set of images from 773 patients. RESULTS: Maximum sensitivity for detection of any retinopathy on a per patient basis was 95.1%, accompanied by specificity of 46.3%. Specificity could be increased as far as 78.9% but was accompanied by a fall in sensitivity to 70.8%. At a setting with 94.8% sensitivity and 52.8% specificity, no cases of sight-threatening retinopathy were missed (retinopathy warranting immediate ophthalmology referral or re-examination sooner than 1 year by National Institute for Clinical Excellence criteria). If the system was implemented at 94.8% sensitivity setting over half the images with no retinopathy would be correctly identified, reducing the need for a human grader to examine images in 1/3 of patients. CONCLUSION: This system could be used when screening for diabetic retinopathy. At 94.8% sensitivity setting the number of normal images requiring examination by a human grader could be halved.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".