Comparative Analysis of Repeatability of Manual and Automated Choroidal Thickness Measurements in Nonneovascular Age-Related Macular Degeneration
Bibliographic record
Abstract
PURPOSE: We compared the reproducibility and mutual agreement of the subfoveal choroidal thickness measurements by expert raters and an automated algorithm in enhanced depth imaging optical coherence tomography (EDI-OCT) images of eyes with nonneovascular age-related macular degeneration (AMD). METHODS: We recruited 44 patients with nonneovascular AMD and EDI-OCT images were acquired. Subfoveal choroidal thickness was measured manually by two expert raters and automatically by a graph-cut-based algorithm. Drusen area was measured using the automated software (version 6) of Cirrus SD-OCT. The manual and automated choroidal thickness measurements were compared in reproducibility, mutual agreement, and correlation with drusen area. RESULTS: The mean subfoveal choroidal thickness was 246 ± 63 μm for the first rater, 214 ± 68 for the second rater, and 209 ± 53 for the automated algorithm. Intraclass correlation coefficients (ICC) and 95% confidence intervals (CI) were 0.96 (CI 0.94-0.98) between the raters, 0.85 (CI 0.77-0.90) between the first rater and the automated algorithm, and 0.84 (CI 0.75-0.89) between the second rater and the automated algorithm. Repeat scan measurement ICCs were 0.91 (CI 0.86-0.94) for the first rater, 0.96 (CI 0.94-0.97) for the second rater, and 0.87 (CI 0.80-0.92) for the automated algorithm. Manual and automated measurements were correlated with drusen area. CONCLUSIONS: The automated algorithm generally yielded smaller choroidal thickness than the raters with a moderate level of agreement. However, its repeat scan measurement repeatability was comparable to that of the manual measurements. The mean difference between the raters indicated possible biases in different raters and rating sessions. The correlation of the automated measurements with the drusen area was comparable to that of the manual measurements. Automated subfoveal choroidal thickness measurement has potential use in clinical practice and clinical trials, with possibility for reduced time and labor cost.
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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 | 0.009 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".