BackgroundArtificial intelligence (AI), which has been used to diagnose diabetic retinopathy (DR), may impact future medical and ophthalmic practices. Therefore, this study explored AI’s general applications and research frontiers in the detection and gradation of DR.MethodsCitation data were obtained from the Web of Science Core Collection database (WoSCC) to assess the application of AI in diagnosing DR in the literature published from January 1, 2012, to June 30, 2022. These data were processed by CiteSpace 6.1.R3 software.ResultsOverall, 858 publications from 77 countries and regions were examined, with the United States considered the leading country in this domain. The largest cluster labeled “automated detection” was employed in the generating stage from 2007 to 2014. The burst keywords from 2020 to 2022 were artificial intelligence and transfer learning.ConclusionInitial research focused on the study of intelligent algorithms used to localize or recognize lesions on fundus images to assist in diagnosing DR. Presently, the focus of research has changed from upgrading the accuracy and efficiency of DR lesion detection and classification to research on DR diagnostic systems. However, further studies on DR and computer engineering are required.

BACKGROUND Retinal vein occlusion (RVO) is the second common cause of blindness following diabetic retinopathy. Patients with RVO often develop macular edema and neovascular glaucoma, which may damage the visual function irreversibly. RVO includes macular retinal vein occlusion, central retinal vein occlusion, and branch retinal vein occlusion. OBJECTIVE Providing patients with an accurate diagnosis, followed by timely and effective treatment is very important for the prognosis of visual function. Therefore, in this paper, we use the Swin Transformer model with a label smoothing method to identify fundus images. METHODS First, 483 and 161 fundus images were used as the training set and the validation set, respectively, to train and regulate the model, whose accuracy reached 98.1%. Additional 161 fundus images were used as the test set to evaluate the model's performance. Next, the area under the receiver operating curve corresponding to macular retinal vein occlusion, central retinal vein occlusion, and branch retinal vein occlusion were obtained using the Swin Transformer model. Finally, we compared the results using the model trained by the deep convolutional neural network. RESULTS The values obtained using the Swin Transformer model for macular retinal vein occlusion, central retinal vein occlusion, and branch retinal vein occlusion were 0.9987, 0.9981, and 0.9974, respectively. The comparison results with other models indicated that the Swin Transformer model performed the best. The results of the study demonstrated that our method can automatically diagnose RVO and determine the type through fundus images, which has the potency to help in the early diagnosis of patients with RVO. CONCLUSIONS Our model can automatically diagnose RVO through fundus images, and its diagnostic accuracy is higher than that of MobileNetV2 and ResNet18. In addition, it can process data sets automatically and efficiently without manual assistance. We can not only diagnose RVO, but also accurately judge its specific type, which has an important clinical significance in real life.