Computer-Based Image Analysis for Plus Disease Diagnosis in Retinopathy of Prematurity
Bibliographic record
Abstract
Presence of plus disease in retinopathy of prematurity (ROP) is an important criterion for identifying ROP requiring treatment. Plus disease is defined by a standard published photograph selected more than 20 years ago by expert consensus. However, diagnosis of plus disease has been shown to be subjective and qualitative. Computer-based image analysis using quantitative methods has potential to improve the objectivity of plus disease diagnosis. The objective was to review the published literature involving computer-based image analysis for ROP diagnosis. The PubMed and Cochrane library databases were searched for the keywords "retinopathy of prematurity" AND "image analysis" AND/OR "plus disease." Reference lists of retrieved articles were searched to identify additional relevant studies. All relevant English-language studies were reviewed. There are four main computer-based systems-ROPtool (area under the receiver operating characteristic curve [AUROC], plus tortuosity 0.95, plus dilation 0.87), RISA (AUROC, arteriolar TI 0.71, venular diameter 0.82), Vessel Map (AUROC, arteriolar dilation 0.75, venular dilation 0.96), and CAIAR (AUROC, arteriole tortuosity 0.92, venular dilation 0.91)-attempting to objectively analyze vessel tortuosity and dilation in plus disease in ROP. Some show promise for identification of plus disease using quantitative methods. This has potential to improve the diagnosis of plus disease and may contribute to the management of ROP using both traditional binocular indirect ophthalmoscopy and image-based telemedicine approaches.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.016 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".