{"type":"ScholarlyArticle","title":"Clinically-Oriented Screening Model for Diabetic Retinopathy Severity Grading and Diabetic Macular Edema Detection","authors":[{"name":"Sanchika Menezes","slug":"sanchika-menezes","given":"Sanchika","family":"Menezes","affiliation":""},{"name":"Rohan Chawla","slug":"rohan-chawla","given":"Rohan","family":"Chawla","affiliation":""},{"name":"Nawazish Shaikh","slug":"nawazish-shaikh","given":"Nawazish","family":"Shaikh","affiliation":""},{"name":"Pradeep Venkatesh","slug":"pradeep-venkatesh","given":"Pradeep","family":"Venkatesh","affiliation":""},{"name":"Radhika Tandon","slug":"radhika-tandon","given":"Radhika","family":"Tandon","affiliation":""},{"name":"Srinivas Rana","slug":"srinivas-rana","given":"Srinivas","family":"Rana","affiliation":""}],"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.","doi":"10.24963/ijcai.2026/819","url":"https://ijcai.org/proceedings/2026/819","pdf":"https://ijcai.org/proceedings/2026/0819.pdf","markdown":"https://ijcai.org/proceedings/2026/819.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/819","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"AI and Social Good","track":"AI and Social Good","number":819,"pages":{"first":7362,"last":7370},"keywords":["Computer Vision: Computer Vision","Multidisciplinary Topics and Applications: Multidisciplinary Topics and Applications","Machine Learning: Machine Learning"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}