DIRD+: A Browser-Based, Offline-First Clinical Platform for Diabetic Retinopathy Screening Using Edge AI Inference in Low-Resource Settings

Background: Diabetic retinopathy (DR) is the leading cause of preventable blindness in working-age adults. In Chile, despite GES coverage since 2006, screening reaches only ~21% of the diabetic population under control. Chilean evidence shows that autonomous AI screening platforms have produced heterogeneous field results (sensitivity 40.8-100%, specificity 55.4%), while Ophthalmic Medical Technologists (TMOs) consistently achieve >97% sensitivity, suggesting AI is most effective as structured support for trained professionals rather than as an autonomous filter. Objective: We present DIRD+ (Diabetic Integrated Retinal Diagnosis), an open-source clinical platform that performs complete DR clinical workflows - patient management, AI-assisted lesion detection, clinical classification, annotation, and report generation - entirely within the web browser using WebAssembly-based inference, without transmitting patient data to any server. This work describes the system architecture and a preliminary technical validation. Methods: DIRD+ implements a six-stage inference pipeline using ONNX Runtime Web (v1.23) with SIMD and multi-thread optimizations, a pluggable clinical guideline engine (ICDR 2024, MINSAL Chile 2017), and a human-in-the-loop annotation workflow. A YOLOv26n detection model was trained on 500 pseudo-labeled APTOS 2019 images using the Annotix framework [11] and evaluated on the IDRiD test set (n=81 images). Results: Optic disc detection - the spatial calibration landmark - achieved AP=1.000 on IDRiD (IoU=0.1). Soft exudate detection achieved AP=0.243 (F1=0.364). Internal validation mAP50=0.578. Browser-based inference averaged 0.297 s/image (3.4 images/second) on CPU without GPU. Lesion detection performance reflects a first-generation model trained on 500 images; progressive improvement through collaborative annotation is ongoing. Conclusions: DIRD+ demonstrates that a complete offline-first DR clinical workflow can be deployed at zero cost within a standard web browser without server infrastructure or GPU. The pluggable guideline engine and human-in-the-loop architecture make DIRD+ a viable tool for TMO-assisted screening in connectivity-limited primary care settings.