---
title: "Clinically-Oriented Screening Model for Diabetic Retinopathy Severity Grading and Diabetic Macular Edema Detection"
authors:
  - "Sanchika Menezes"
  - "Rohan Chawla"
  - "Nawazish Shaikh"
  - "Pradeep Venkatesh"
  - "Radhika Tandon"
  - "Srinivas Rana"
doi: "10.24963/ijcai.2026/819"
url: "https://ijcai.org/proceedings/2026/819"
pdf: "https://ijcai.org/proceedings/2026/0819.pdf"
venue: "Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence"
year: 2026
pages: "7362-7370"
track: "AI and Social Good"
keywords:
  - "Computer Vision: Computer Vision"
  - "Multidisciplinary Topics and Applications: Multidisciplinary Topics and Applications"
  - "Machine Learning: Machine Learning"
---

# Clinically-Oriented Screening Model for Diabetic Retinopathy Severity Grading and Diabetic Macular Edema Detection

Sanchika Menezes, Rohan Chawla, Nawazish Shaikh, Pradeep Venkatesh, Radhika Tandon, Srinivas Rana

*Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence*, pages 7362-7370. DOI: [10.24963/ijcai.2026/819](https://doi.org/10.24963/ijcai.2026/819)

## Abstract

Diabetic retinopathy (DR) and diabetic macular edema (DME) are leading causes of preventable blindness worldwide. Automated screening tools are critical for timely detection at scale, particularly in low-resource settings where access to ophthalmologists is limited. We propose DRDME-Net, a deployment-driven joint learning framework that formulates DR grading as an ordinal regression task and DME detection via a continuous surrogate, rather than conventional classification. This design yields stable risk scores tightly aligned with operational clinical decision-making thresholds. Evaluation on facility and community cohorts demonstrates that DRDME-Net achieves strong performance across severity boundaries. Insights from an initial feasibility pilot further demonstrate its scalability in real-world workflows. These results highlight the potential of DRDME-Net to expand equitable access to timely detection, reduce preventable vision loss, and provide a practical template for integrating AI into population screening initiatives.