Purpose.To measure the concentrations of various cytokines in the aqueous humor from patients with different stages of diabetic retinopathy.Methods.All selected cataract patients were categorized into 4 groups: the control group (patients without diabetes), nonretinopathy (NDR) group (diabetic patients without retinopathy), nonproliferative diabetic retinopathy (NPDR) group, and proliferative diabetic retinopathy (PDR) group. The aqueous concentrations of interleukin- (IL-) 1β, IL-2, IL-4, IL-5, IL-6, IL-10, interferon-γ, tumor necrosis factor-α, and vascular endothelial growth factor (VEGF) from patients were measured using the cytometric bead array technique.Results.In this study, 10, 22, 15, and 14 patients were included in the control, NDR, NPDR, and PDR groups, respectively. No difference was observed in the aqueous concentrations of all cytokines between the control group and the NDR group. By contrast, comparison of these groups revealed that the aqueous concentrations of most inflammatory cytokines were significantly higher in the PDR and NPDR groups. In addition, the concentrations of IL-2, IL-5, and VEGF were higher in the PDR group than those in the NPDR group.Conclusions.Aqueous concentrations of various cytokines increased with the severity of patients’ diabetic retinopathy. This finding implies that these cytokines might play a role in the progression of diabetic retinopathy.

Deep learning evolves into a new form of machine learning technology that is classified under artificial intelligence (AI), which has substantial potential for large-scale healthcare screening and may allow the determination of the most appropriate specific treatment for individual patients. Recent developments in diagnostic technologies facilitated studies on retinal conditions and ocular disease in metabolism and endocrinology. Globally, diabetic retinopathy (DR) is regarded as a major cause of vision loss. Deep learning systems are effective and accurate in the detection of DR from digital fundus photographs or optical coherence tomography. Thus, using AI techniques, systems with high accuracy and efficiency can be developed for diagnosing and screening DR at an early stage and without the resources that are only accessible in special clinics. Deep learning enables early diagnosis with high specificity and sensitivity, which makes decisions based on minimally handcrafted features paving the way for personalized DR progression real-time monitoring and in-time ophthalmic or endocrine therapies. This review will discuss cutting-edge AI algorithms, the automated detecting systems of DR stage grading and feature segmentation, the prediction of DR outcomes and therapeutics, and the ophthalmic indications of other systemic diseases revealed by AI.