Abstract P400: Screening for Hypertension Using Retinal Vascular Calibre in Ultra-Widefield Fundus Imaging
Bibliographic record
Abstract
Fundus images from the left eyes of 440 subjects aged 50-59 years enrolled in the Northern Ireland Cohort of Longitudinal Ageing were analyzed. Subjects were categorized as normotensive or hypertensive, according to thresholds on systolic/diastolic blood pressure measurement (140/90 mm Hg) averaged over two sitting measurements in a clinical setting. A fully automatic system to analyze each image used conventional and deep neural network machine learning techniques to locate retinal landmarks and detect, classify and measure retinal vessels. From this data, a measure of the arteriolar-venular ratio (AVR) in the peripheral retina was calculated. Semi-automatic analysis was also performed. Results are presented in Table 1. Subjects had mean age of 54.6 ± 2.9 years; 56.1% (247 of 440) females, with 34.3% (151 of 440) subjects categorized as hypertensive. Narrower arterioles and smaller AVR were observed in subjects with hypertension. This was also observed in fully-automated analysis, however 4% (17 of 440) subjects failed to be processed by the system. In fully-automated analysis the area under a receiver operator characteristic curve of AVR for hypertensive status was 0.69 (95% CI, 0.63 to 0.74). Table 1 - Results for semi-automated and automated analysis of retinal vessel parameters. *p<0.005 Automated measurement of AVR in ultra-widefield fundus imaging was associated with hypertension. With further development, such as evaluation against diagnosis of hypertension obtained from ambulatory blood pressure monitoring clinics, this system could become a test for undiagnosed hypertension in people attending routine eye health check-ups.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".