Quantification of Fundus Autofluorescence Features in a Molecularly Characterized Cohort of >3500 Patients with Inherited Retinal Disease from the United Kingdom
Bibliographic record
Abstract
Purpose: To quantify relevant fundus autofluorescence (FAF) features cross-sectionally and longitudinally in a large cohort of patients with inherited retinal diseases (IRDs).Design: Retrospective study of imaging data.Participants: Patients with a clinical and molecularly confirmed diagnosis of IRD who have undergone 55 FAF imaging at Moorfields Eye Hospital (MEH) and the Royal Liverpool Hospital between 2004 and 2019.Methods: Five FAF features of interest were defined: vessels, optic disc, perimacular ring of increased signal (ring), relative hypo-autofluorescence (hypo-AF), and hyper-autofluorescence (hyper-AF).Features were manually annotated by 6 graders in a subset of patients based on a defined grading protocol to produce segmentation masks to train an artificial intelligence model, AIRDetect, which was then applied to the entire imaging data set.Main Outcome Measures: Quantitative FAF features, including area and vessel metrics, were analyzed cross-sectionally by gene and age, and longitudinally.AIRDetect feature segmentation and detection were validated with Dice score and precision/recall, respectively.Results: A total of 45 749 FAF images from 3606 patients with IRD from MEH covering 170 genes were automatically segmented using AIRDetect.Model-grader Dice scores for the disc, hypo-AF, hyper-AF, ring, and vessels were, respectively, 0.86, 0.72, 0.69, 0.68, and 0.65.Across patients at presentation, the 5 genes with the largest hypo-AF areas were CHM, ABCC6, RDH12, ABCA4, and RPE65, with mean per-patient areas of 43.72, 29.57, 20.07, 19.65, and 16.92 mm 2 , respectively.The 5 genes with the largest hyper-AF areas were BEST1, CDH23, NR2E3, MYO7A, and RDH12, with mean areas of 0.50, 047, 0.44, 0.38, and 0.33 mm 2 , respectively.The 5 genes with the largest ring areas were NR2E3, CDH23, CRX, EYS, and PDE6B, with mean areas of 3.60, 2.90, 2.89, 2.56, and 2.20 mm 2 , respectively.Vessel density was found to be highest in EFEMP1, BEST1, TIMP3, RS1, and PRPH2 (11.0%, 10.4%, 10.1%, 10.1%, 9.2%) and was lower in retinitis pigmentosa (RP) and Leber congenital amaurosis genes.Longitudinal analysis of decreasing ring area in 4 RP genes (RPGR, USH2A, RHO, and EYS) found EYS to be the fastest progressor at À0.178 mm 2 /year. Conclusions:We have conducted the first large-scale cross-sectional and longitudinal quantitative analysis of FAF features across a diverse range of IRDs using a novel AI approach.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".