Using Fused Data from Perimetry and Optical Coherence Tomography to Improve the Detection of Visual Field Progression in Glaucoma
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Perimetry and optical coherence tomography (OCT) are both used to monitor glaucoma progression. However, combining these modalities can be a challenge due to differences in data types. To overcome this, we have developed an autoencoder data fusion (AEDF) model to learn compact encoding (AE-fused data) from both perimetry and OCT. The AEDF model, optimized specifically for visual field (VF) progression detection, incorporates an encoding loss to ensure the interpretation of the AE-fused data is similar to VF data while capturing key features from OCT measurements. For model training and evaluation, our study included 2504 longitudinal VF and OCT tests from 140 glaucoma patients. VF progression was determined from linear regression slopes of longitudinal mean deviations. Progression detection with AE-fused data was compared to VF-only data (standard clinical method) as well as data from a Bayesian linear regression (BLR) model. In the initial 2-year follow-up period, AE-fused data achieved a detection F1 score of 0.60 (95% CI: 0.57 to 0.62), significantly outperforming (p < 0.001) the clinical method (0.45, 95% CI: 0.43 to 0.47) and the BLR model (0.48, 95% CI: 0.45 to 0.51). The capacity of the AEDF model to generate clinically interpretable fused data that improves VF progression detection makes it a promising data integration tool in glaucoma management.
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.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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