Optical coherence tomography homography for detection of retinal displacement: a validation study
Bibliographic record
Abstract
PURPOSE: Retinal displacement following rhegmatogenous retinal detachment (RRD) has been associated with inferior functional outcomes. Recent evidence using an overlay technique suggests that fundus-autofluorescence underestimates post-RRD repair retinal displacement. This study aims to validate the overlay technique in normal eyes and to determine its sensitivity and specificity at detecting retinal displacement. METHODS: We conducted a retrospective case series involving 66 normal eyes, each with at least two separate infrared (IR) images at different time points. Overlay of the two images was based on manual marking of choroidal and optic nerve head (ONH) landmarks. For each set of two IR images, computer code for homography generated two outputs, flipping view video and an overlay picture. First, validation of choroidal/ONH alignment was performed using the flipping view video to ensure accurate manual markings. Then, two different masked graders (AB + IM) evaluated the overlays for presence of retinal displacement. 16 control eyes following RRD repair with detected retinal displacement on FAF imaging assessed sensitivity and specificity of the technique. RESULTS: 94% of overlays were found to be well aligned (62/66). 11 cases exhibited errors on flipping view analysis (choroidal/ONH misalignment). Those 11 cases had a significantly higher rate of retinal displacement (false positives) compared to cases without errors (8/11,72% Vs 54/55,98%,P = 0.001). Sensitivity and specificity of the overlay technique for detecting retinal displacement considering only adequate flipping view cases (n = 55) were calculated as 100% and 98%, respectively. CONCLUSIONS: IR overlay emerges as a reliable and valid method for detecting retinal displacement, exhibiting excellent sensitivity and specificity.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".