Deep Learning with Disc Photos or OCT Scans in Glaucoma Detection
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Bibliographic record
Abstract
Objective: To determine whether a deep learning (DL) model using retinal nerve fiber layer thickness (RNFLT) maps from OCT scans can detect glaucoma, defined by functional visual field (VF) impairment, more accurately than a DL model using disc photos (DPs). A secondary objective was to assess the diagnostic performance of these DL models across demographic groups (race, sex, and ethnicity). Design: Retrospective cohort study at a tertiary glaucoma center utilizing OCT and DP datasets collected between 2011 and 2022. Participants: Out of the 16 936 DP and OCT image sets, patients with Cirrus OCT images with a quality score ≥6 of 10 and reliable 24-2 Humphrey VF tests (fixation loss ≤33%, false-negative rate ≤20%, false-positive rate ≤20%), taken within 30 days of OCT, were included. Disc photos were obtained within 6 months of OCT. Data were randomly selected for training and testing of the DL models. Testing: Development of DL models utilizing either OCT RNFLT maps or DPs to detect glaucoma based on VF-defined functional impairment. Main Outcome Measures: The primary outcome was the area under the curve (AUC) for glaucoma detection, comparing the OCT-based DL model with the DP-based model. The secondary outcome was the AUC across demographic groups. Results: < 0.005). Conclusions: When glaucoma diagnosis was based on functional deficit, the OCT-based DL model offered greater accuracy in detecting glaucoma than the DP-based model, likely due to its use of objective and quantitative RNFLT measurements. This work supports the use of OCT-based DL models for glaucoma detection, while observed demographic disparities underscore the need for equitable datasets to ensure fair DL-driven glaucoma diagnosis across populations. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it