This invention discloses an early screening and intelligent follow-up
management system and method for
diabetic retinopathy (DR), belonging to the field of
artificial intelligence in
medicine and primary care
chronic disease management. The
system consists of a
fundus image acquisition terminal, a lightweight DR AI diagnostic module, a multimodal
physiological monitoring unit, an intelligent follow-up and early warning engine, a hierarchical diagnosis and treatment scheduling platform, and a cloud-edge collaborative
server. The DR AI diagnostic module is built based on EfficientNetV2-B0, with a
file size ≤15MB after INT8 quantization and a single-image
inference time ≤50ms, allowing it to run offline on mobile phones and tablets. After training on 10386 labeled fundus images, it achieves an accuracy of 93.7%, an AUC of 0.952, and a sensitivity of 91.3%, effectively identifying early lesions. The
system connects to devices such as
blood glucose meters and
blood pressure monitors to achieve real-time monitoring of physiological indicators, pushing early warnings within 10 seconds of abnormalities, improving follow-up compliance, and establishing a
closed loop of "
primary screening at the grassroots level—higher-level diagnosis and treatment—
community follow-up," thereby improving the efficiency of primary care
chronic disease screening and management.