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Diabetic retinopathy (DR) is one of the major causes of visual impairment in adults with diabetes. Optical coherence tomography angiography (OCTA) is nowadays widely used as the golden criterion for diagnosing DR. Recently, wide-field OCTA (WF-OCTA) provided more abundant information including that of the peripheral retinal degenerative changes and it can contribute in accurately diagnosing DR. The need for an automatic DR diagnostic system based on WF-OCTA pictures attracts more and more attention due to the large diabetic population and the prevalence of retinopathy cases. In this study, automatic diagnosis of DR using vision transformer was performed using WF-OCTA images (12 mm × 12 mm single-scan) centered on the fovea as the dataset. WF-OCTA images were automatically classified into four classes: No DR, mild nonproliferative diabetic retinopathy (NPDR), moderate to severe NPDR, and proliferative diabetic retinopathy (PDR). The proposed method for detecting DR on the test set achieves accuracy of 99.55%, sensitivity of 99.49%, and specificity of 99.57%. The accuracy of the method for DR staging reaches up to 99.20%, which has been proven to be higher than that attained by classical convolutional neural network models. Results show that the automatic diagnosis of DR based on vision transformer and WF-OCTA pictures is more effective for detecting and staging DR. [ABSTRACT FROM AUTHOR]

Pathological myopia (PM) is a severe ocular disease leading to blindness. As a traditional noninvasive diagnostic method, fundus color photography (FCP) is widely used in detecting PM due to its high fidelity and precision. However, manual examination of fundus photographs for PM is time-consuming and prone to high error rates. Existing automated detection technologies have yet to study the detailed classification in diagnosing different stages of PM lesions. In this paper, we proposed an intelligent system which utilized Resnet101 technology to multi-categorically diagnose PM by classifying FCPs with different stages of lesions. The system subdivided different stages of PM into eight subcategories, aiming to enhance the precision and efficiency of the diagnostic process. It achieved an average accuracy rate of 98.86% in detection of PM, with an area under the curve (AUC) of 98.96%. For the eight subcategories of PM, the detection accuracy reached 99.63%, with an AUC of 99.98%. Compared with other widely used multi-class models such as VGG16, Vision Transformer (VIT), EfficientNet, this system demonstrates higher accuracy and AUC. This artificial intelligence system is designed to be easily integrated into existing clinical diagnostic tools, providing an efficient solution for large-scale PM screening. [ABSTRACT FROM AUTHOR]