Ophthalmology Assistant

Overview

We developed the Ophthalmology Assistant Agent, an AI-powered clinical tool designed to assist eye care professionals in screening, triaging, and managing vision-related disorders. The system leverages deep learning to analyze fundus images and visual field data, automate diagnosis suggestions, and generate structured, interoperable reports—significantly improving speed and accuracy in ophthalmic decision-making. The solution was created in partnership with Shifa Hospital to address critical workflow bottlenecks in diagnosing and monitoring conditions like diabetic retinopathy, glaucoma, and macular degeneration.

Technologies

Next JS

Python

Langchain

PostgreSQL

The Challenges

Ophthalmologists face multiple challenges in delivering timely and accurate diagnoses:

Manual Scan Interpretation: Fundus and visual field tests require expert-level interpretation, which is time-consuming and not always scalable.
Delayed Diagnosis: Shortages of specialists in many regions delay care and increase the risk of irreversible vision loss.
Inconsistent Documentation: Variability in reporting leads to inefficiencies and lack of standardization across providers.
Lack of Integration: Many tools don’t sync with EMRs, making patient follow-up and care coordination difficult.

Our Unique Approach

1. Retinal Imaging Analysis

AI models detect signs of retinal pathologies like diabetic retinopathy and glaucoma directly from fundus images. Each image is segmented, scored by severity, and flagged for review.

2. Visual Field Interpretation

The system evaluates visual field test results for trend detection and visual loss patterns, offering insights for glaucoma monitoring.

3. Pathology Classification & Triage

Images are auto-classified into normal/pathological categories with risk grading, enabling high-volume screenings and urgent case prioritization.

4. Follow-Up Recommendations

Based on image results and patient history, the system suggests follow-up schedules, specialist referrals, or treatment escalation, integrating seamlessly into clinical workflows.

5. Interoperable Reporting

The agent generates structured diagnostic reports compatible with DICOM, HL7, and PDF formats—ready for EMR upload or referral use.

‍Results

Improved diagnostic consistency across high-volume screenings
Faster turnaround times from imaging to diagnosis and referral
Reduced manual workload for ophthalmologists in triaging and reporting
Earlier detection of conditions like diabetic retinopathy and glaucoma
Seamless EHR integration with one-click report syncing

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