End-to-end diabetic retinopathy grading based on fundus fluorescein angiography images using deep learning

Purpose To develop and validate a deep learning system for diabetic retinopathy (DR) grading based on fundus fluorescein angiography (FFA) images. Methods A total of 11,214 FFA images from 705 patients were collected to form the internal dataset. Three convolutional neural networks, namely VGG16, RestNet50, and DenseNet, were trained using a nine-square grid input, and heat maps were generated. Subsequently, a comparison between human graders and the algorithm was performed. Lastly, the best model was tested on two external datasets (Xian dataset and Ningbo dataset). Results VGG16 performed the best, with a maximum accuracy of 94.17%, and had an AUC of 0.972, 0.922, and 0.994 for levels 1, 2, and 3, respectively. For Xian dataset, our model reached the accuracy of 82.47% and AUC of 0.910, 0.888, and 0.976 for levels 1, 2, and 3. As for Ningbo dataset, the network performed with the accuracy of 88.89% and AUC of 0.972, 0.756, and 0.945 for levels 1, 2, and 3. Conclusions A deep learning system for DR staging was trained based on FFA images and evaluated through human-machine comparisons as well as external dataset testing. The proposed system will help clinical practitioners to diagnose and treat DR patients, and lay a foundation for future applications of other ophthalmic or general diseases.

Automated summary

A deep learning system based on fundus fluorescein angiography (FFA) images was developed and validated for grading diabetic retinopathy (DR). The system achieved high accuracy and AUC values on both internal and external datasets. It has the potential to assist clinical practitioners in diagnosing and treating DR patients, and lays a foundation for future applications in ophthalmic and general diseases.