DeepRet: A Portable and Multimodal AI System for Enhancing Glaucoma Diagnosis in Resource-Limited Settings
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
Glaucoma, projected to impact over 110 million individuals by 2040, remains a significant challenge in low-resource settings due to limited access to diagnostic tools and physicians, delaying early detection. Automated glaucoma diagnosis systems have emerged as potentially scalable solutions but often rely on single-modality imaging and lack integration with real-world patient interactions. To bridge these gaps, DeepRet, an AI-based system, leverages multimodal data—retinal imaging, age, and intraocular pressure measurements—to achieve 96.34% accuracy, a 93% AUROC, and a 17.30% improvement over traditional methods. Designed for low-resource environments, DeepRet features an affordable, portable retinal imager with a manufacturing cost under $200, 3D printed materials, and low-cost electronics. Its intuitive design minimizes training requirements for health workers while offering real-time, patient-friendly feedback, enhancing accessibility and the patient experience.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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