Overview
Optical coherence tomography and fundus photography are central to diagnosing diabetic retinopathy AMD and other retinal diseases. AI algorithms automate detection segmentation and progression assessment to support screening and triage. Teleophthalmology expands access to retinal screening in primary care and underserved areas.
AI Applications
Automated OCT layer segmentation and fluid detection accelerate diagnosis and quantify treatment response in macular disease. Screening algorithms for diabetic retinopathy identify referable disease from fundus images with high sensitivity. Integrate AI outputs into referral workflows with human oversight to ensure safety.
Clinical Pathways
Implement screening programs that combine primary care capture with centralized AI triage and ophthalmologist review for positives. Use quantitative OCT metrics to guide anti VEGF therapy intervals and to monitor recurrence. Ensure image quality control and standardized acquisition protocols for reliable AI performance.
Access and Equity
Validate AI models across diverse populations and imaging devices to avoid disparities in detection performance. Provide training and support for primary care staff and ensure clear referral pathways for positive screens. Monitor program outcomes and adjust thresholds to balance sensitivity and resource capacity.