# Targeting vision-threatening disease in EyeArt-based diabetic retinopathy screening: A letter in response to: “Evaluation of the degree of agreement in the diagnosis of diabetic retinopathy between ophthalmologists and EyeArt”

## TL;DR

This letter argues that diabetic retinopathy screening should focus on preventing vision-threatening cases rather than all cases, to better balance accuracy and efficiency.

## Contribution

The paper proposes prioritizing vision-threatening DR as the primary endpoint in screening programs and suggests integrating multimodal AI systems for better triage.

## Key findings

- Current screening metrics optimized for any DR may not balance sensitivity, specificity, and referral burden effectively.
- OCT-based risk factors support the development of AI systems combining imaging and clinical data to triage vtDR.
- Multimodal AI could enable more efficient screening and personalized follow-up intervals.

## Abstract

Guedes et al. demonstrate excellent agreement between EyeArt and ophthalmologist grading for any diabetic retinopathy (DR) within a regional screening program, with near-perfect binocular sensitivity and high specificity. Building on these findings, this letter argues that screening programs are primarily justified by the prevention of vision-threatening DR (vtDR), and that performance metrics should therefore prioritize vtDR rather than any DR as the primary endpoint. The letter highlights how the current operating point, optimized for any DR, may not reflect an optimal balance between sensitivity, specificity, and downstream referral burden in real-world workflows. It also discusses how the study’s OCT-based risk factors support the development of multimodal and risk-integrated AI systems that combine fundus imaging, OCT, and clinical variables to triage vtDR more efficiently and personalize screening intervals.