Quantification of RPE Changes in Choroideremia Using a Photoshop-Based Method
Bibliographic record
Abstract
Purpose: To develop a reliable and efficient method for quantifying the area of preserved retinal pigment epithelium (RPE), facilitating the evaluation of disease progression or response to therapy in choroideremia (CHM). Methods: The fundus autofluorescence images of CHM patients were captured at baseline and 1 year. A Photoshop-based method was developed to allow the reliable measurement of the RPE area. The results were compared with measurements generated by the Heidelberg Eye Explorer 2 (HEYEX2). The areas measured by two independent graders were compared to assess the test-retest reliability. Results: annually, and a percentage of 8.39% ± 5.24%. The average standard deviations for Photoshop were less than that for HEYEX2 (0.5-1.1 in grader 1; 0.4-1.6 in grader 2), indicating less intragrader variability. The RPE decrease as determined by the Photoshop-based method showed excellent reliability with an intraclass correlation coefficient of 0.944 (95% confidence interval, 0.907-0.966). In Bland-Altman plots, the Photoshop method also exhibited better intergrader agreement. Conclusions: Photoshop-based quantification of preserved RPE area in patients with CHM is feasible and has better test-retest reliability compared with the HEYEX2 method. Translational Relevance: An accurate quantification method for longitudinal RPE change in CHM patients is an important tool for the evaluation of efficacy in any therapeutic trials.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".