A Teleophthalmology Screening Platform for Diabetic Retinopathy with Lesion-Based Evidence and Ordinal Grading - Bernardes, Laura; Heckler, Artur; Pereira, Alejandro; Dias, Marcelo; Aguiar, Marilton; Welfer, Daniel; Santos, Carlos (Anais do Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS), 2026)
Diabetic retinopathy screening in public health settings demands scalable workflows, traceability, and clinically meaningful visual evidence. This paper presents an open teleophthalmology platform that couples multi-label lesion segmentation with ordinal DR grading (0–4) and exposes the inference trail through an interactive interface. Using the public IDRiD dataset, we evaluated segmentation performance across U-Net++, Attention U-Net, and Swin-Unet, and grading performance with EfficientNetV2-S and ConvNeXt-Tiny, including QWK, Macro-F1, Weighted-F1, AUC (OvR), per-class reports, and confusion matrices. Beyond model metrics, the contribution is a healthcare-oriented system design that operationalizes visual evidence, class-wise threshold calibration, and experiment governance, enabling remote triage scenarios and supporting future external and clinical validation in partner ophthalmology clinics.