Measurement of sub-foveal choroidal thickness from optical low coherence reflectometry biometry: a new method
Bibliographic record
Abstract
It is estimated that by the year 2050, approximately 50.0 % of the world’s population will be myopic. It has been suggested that the thickening sub-foveal choroidal thickness (SFChT) is a precursor for reduced eye growth and slowed myopia progression. Hence, it is highly important to identify structural changes, during myopia management. Literature suggests that the SFChT show short-term changes and has been proposed as an ocular marker for many ocular conditions including pathological myopia. A major limitation to its use is that none of the commercially available instruments give a direct measure of SFChT. This paper describes a new semi-automated method for quantification of the SFChT using ocular biometry. This image processing method is used on healthy pediatric myopes to quantify the SFChT. Both axial length (AXL) and SFChT were quantified from a 2-D A-scan graph generated from an ocular biometer (ARGOS, Aichi, Japan). An experienced clinician manually selected three peak points corresponding to anterior corneal, retinal, and choroidal peaks which were used as the input to the algorithm. Using the pixel properties, the overall AXL was calculated by subtracting the distance between the anterior corneal and retinal peak. Similarly, the SFChT was calculated as the distance difference between retinal and choroidal peaks. These calculated values were compared with a standard clinical method. The intraclass correlation coefficient ICC showed a good (κ=0.79, CI: 0.63 – 0.87) agreement between the methods. Similarly, the Bland-Altman plot showed a good agreement (The mean difference between the two methods was -21.28 μm) with a wide limit of agreement (LOA: 79.08 & -121.64 μm). Compared to SS-OCT method and new-semi automated method, there is significant overestimation of SFChT (p<0.001). However, there is moderate agreement between these two methods.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".