The retinal age gap: an affordable and highly accessible biomarker for population-wide disease screening across the globe
Bibliographic record
Abstract
Traditional biomarkers, such as those obtained from blood tests, are essential for early disease detection, improving health outcomes and reducing healthcare costs. However, they often involve invasive procedures, specialized laboratory equipment or special handling of biospecimens. The retinal age gap (RAG) has emerged as a promising new biomarker that can overcome these limitations, making it particularly suitable for disease screening in low- and middle-income countries. This study aimed to evaluate the potential of the RAG as a biomarker for broad disease screening across a vast spectrum of diseases. Fundus images were collected from 86 522 UK Biobank participants aged 40-83 (mean age: 56.2 ± 8.3 years). A deep learning model was trained to predict retinal age using 17 791 images from healthy participants. The remaining images were categorized into disease/injury groups based on clinical codes. Additionally, 8524 participants from the Brazilian Multilabel Ophthalmological Dataset (BRSET) were used for external validation. Among the 159 disease/injury groups from the 2019 Global Burden of Disease Study, 56 groups (35.2%) exhibited RAG distributions significantly different from healthy controls. Notable examples included chronic kidney disease, cardiovascular disease, blindness, vision loss and diabetes. Overall, the RAG shows great promise as a cost-effective, non-invasive biomarker for early disease screening.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".