Diagnostic Performance for Detection of Glaucomatous Structural Damage Using Pixelwise Analysis of Retinal Thickness Measurements
Bibliographic record
Abstract
Purpose: To compare the diagnostic accuracy of thickness measurements of individual and combined macular retinal layers to discriminate 188 glaucomatous and 148 glaucoma suspect eyes from 362 healthy control (HC) eyes on a pixel-by-pixel basis. Methods: For this retrospective study, we manually corrected the segmentations of posterior pole optical coherence tomography (OCT) scans to determine the thickness of the nerve fiber layer (NFL), ganglion cell layer (GCL), inner plexiform layer (IPL), the ganglion cell complex (GCC), and the total neural retina (TR). For each eye, the total number of pixels with thickness values less than the fifth percentile of the HC distribution was used to create a receiver operating characteristic (ROC) curve for each layer and for layer combinations. Results: Using total abnormal pixel count criteria to discriminate glaucoma from HC eyes, the individual layers with the highest area under the ROC curve (AUC) were the NFL and GCL; IPL performance was significantly lower (P < 0.05). GCC had a significant higher AUC (94.3%) than individual the AUC of the NFL (92.3%) (P = 0.0231) but not higher than AUC of the GCL (93.4%) (P = 0.3487). The highest AUC (95.4%) and sensitivity (85.1%) at 95% specificity was found for the Boolean combination of NFL or GCL. The highest AUC is not significantly higher (P = 0.0882) than the AUC of the GCC but the highest sensitivity is significantly higher than the sensitivity of the GCC. This pattern was similar for discriminating between suspect and HC eyes (P = 0.0356). Conclusions: Using pixel-based methods, the diagnostic accuracy of NFL and GCL exceeded that of IPL and TR. GCC had equivalent performance as NFL and GCL. The specific spatial locations within the posterior pole that exhibit best performance vary depending on which layer is being assessed. Recognizing this dependency highlights the importance of considering multiple layers independently, as they offer complementary information for effective and comprehensive diagnosis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".