Automated Glaucoma Pre-Screening
Regional Eye Care Network
AI-assisted classification of retinal fundus images for glaucoma staging in primary care settings.
Primary care clinics lacked the specialist expertise to perform early glaucoma screening. Patients waited weeks for ophthalmologist referrals, and many cases of early-stage glaucoma went undetected. The clinic network needed a way to pre-screen retinal images at the point of capture to prioritize urgent referrals.
A Moondream model fine-tuned on labeled retinal fundus images classifies eyes as normal, early-stage, or advanced glaucoma. Deployed on-premises at clinic locations for data privacy compliance, the model provides immediate pre-screening results that help clinicians prioritize specialist referrals.
- Screening accuracy improved from 37.5% to 72.5%
- Pre-screening results available at point of capture
- Reduced specialist referral wait times for high-risk patients
- On-premises deployment satisfies healthcare data privacy requirements
Complete Vision AI Stack
This solution uses Moondream's integrated stack from model training through production deployment. Every layer is designed to work together, so you go from problem to deployed system without stitching together tools from different vendors.
AI Model Layer
Base Model
Moondream 3
Fine-Tuning
RL via Lens
Production Model
Moondream 2
Deployment Layer
Inference Engine
Photon
Target Hardware
Intel Xeon CPU
Deployment
On-Premises
Training Method
RL
Training Steps
100
Task Type
query
Accuracy
17.6% → 69.2%
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