A Deep Learning Approach to Accurately Discriminate Between Optic Disc Drusen and Papilledema on Fundus Photographs
Bibliographic record
Abstract
Abstract Objective To assess the performance of a deep learning system (DLS) to discriminate between optic disc drusen (ODD) and papilledema caused by intracranial hypertension, using standard color ocular fundus photographs collected in a large international multi-ethnic population. Design Retrospective study. Participants The study included 4,508 color fundus images in 2,180 patients from 30 neuro-ophthalmology centers (19 countries) participating in the Brain and Optic Nerve Study with Artificial Intelligence (BONSAI) Group. Methods We trained, validated, and tested a dedicated DLS for binary classification of ODD vs. papilledema (including various subgroups within each category), on conventional mydriatic digital ocular fundus photographs. For training and internal validation, we used 857 ODD images and 3,230 papilledema images, in 1,959 patients. External-testing was subsequently performed on an independent dataset (221 patients) including 207 images with ODD (96 visible and 111 buried), provided by 3 centers of the Optic Disc Drusen Studies Consortium, and 214 images of papilledema (92 mild-to-moderate and 122 severe) from a previously validated study. Main outcome measures Area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were used to discriminate between ODD and papilledema. Results Overall, the DLS could accurately distinguish between all ODD and papilledema (all severities included): AUC 0.97 (95% confidence interval [CI], 0.96 to 0.98), accuracy 90.5% (95% CI, 88.0% to 92.9%), sensitivity 86.0% (95% CI, 82.1% to 90.1%), and specificity 94.9% (95% CI, 92.3% to 97.6%). The performance of the DLS remained high for discrimination of buried ODD from mild-to-moderate papilledema: AUC 0.93 (95% CI, 0.90 to 0.96), accuracy 84.2% (95% CI, 80.2%-88.6%), sensitivity 78.4% (95% CI, 72.2% to 84.7%), and specificity 91.3% (95% CI, 87.0% to 96.4%). Conclusions A dedicated DLS can accurately distinguish between ODD and papilledema caused by elevated intracranial pressure, even when considering buried ODD vs mild-to-moderate papilledema. Future studies are required to validate the utility of this DLS in clinical practice.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".