Diabetic retinopathy (DR) is one of the microvascular complications of diabetes mellitus (DM). Like other macrovascular complications of DM, the development and progression of DR is influenced by a variety of systemic and local factors. It is essential to understand the importance of multidisciplinary collaboration. Systemic risk fators such as hyperglycemia, hypertension, dyslipidemia and diabetic nephropathy should be treated before effective DR management can be implemented. Through multidisciplinary collaboration, we can prevent the development of DR, slow the progression of DR, and improve the safety of perioperative care. Thereby enhancing the level of prevention and control of DM complications, including DR.
Diabetic macular edema is the leading cause of central vision loss and even blindness in diabetic retinopathy. Compared to FFA, OCT can obtain the high-resolution 3D image quickly, easily to reflect the details of the tissue and realize the quantitative measurement. As a novel technology, OCT angiography (OCTA) can display microvascular structure from different layers of retina and choroid, having its advantage of quantifying the vessel density and the lesion area. By detecting fundus morphology, quantifying and quantitating the retinal vessels and vessel density, the combination of OCT and OCTA could play a guiding role in diagnosis, classification, treatment and prognosis of diabetic macular edema.
ObjectiveTo compare the consistency of artificial analysis and artificial intelligence analysis in the identification of fundus lesions in diabetic patients.MethodsA retrospective study. From May 2018 to May 2019, 1053 consecutive diabetic patients (2106 eyes) of the endocrinology department of the First Affiliated Hospital of Zhengzhou University were included in the study. Among them, 888 patients were males and 165 were females. They were 20-70 years old, with an average age of 53 years old. All patients were performed fundus imaging on diabetic Inspection by useing Japanese Kowa non-mydriatic fundus cameras. The artificial intelligence analysis of Shanggong's ophthalmology cloud network screening platform automatically detected diabetic retinopathy (DR) such as exudation, bleeding, and microaneurysms, and automatically classifies the image detection results according to the DR international staging standard. Manual analysis was performed by two attending physicians and reviewed by the chief physician to ensure the accuracy of manual analysis. When differences appeared between the analysis results of the two analysis methods, the manual analysis results shall be used as the standard. Consistency rate were calculated and compared. Consistency rate = (number of eyes with the same diagnosis result/total number of effective eyes collected) × 100%. Kappa consistency test was performed on the results of manual analysis and artificial intelligence analysis, 0.0≤κ＜0.2 was a very poor degree of consistency, 0.2≤κ＜0.4 meant poor consistency, 0.4≤κ＜0.6 meant medium consistency, and 0.6≤κ＜1.0 meant good consistency.ResultsAmong the 2106 eyes, 64 eyes were excluded that cannot be identified by artificial intelligence due to serious illness, 2042 eyes were finally included in the analysis. The results of artificial analysis and artificial intelligence analysis were completely consistent with 1835 eyes, accounting for 89.86%. There were differences in analysis of 207 eyes, accounting for 10.14%. The main differences between the two are as follows: (1) Artificial intelligence analysis points Bleeding, oozing, and manual analysis of 96 eyes (96/2042, 4.70%); (2) Artificial intelligence analysis of drusen, and manual analysis of 71 eyes (71/2042, 3.48%); (3) Artificial intelligence analyzes normal or vitreous degeneration, while manual analysis of punctate exudation or hemorrhage or microaneurysms in 40 eyes (40/2042, 1.95%). The diagnostic rates for non-DR were 23.2% and 20.2%, respectively. The diagnostic rates for non-DR were 76.8% and 79.8%, respectively. The accuracy of artificial intelligence interpretation is 87.8%. The results of the Kappa consistency test showed that the diagnostic results of manual analysis and artificial intelligence analysis were moderately consistent (κ=0.576, P＜0.01).ConclusionsManual analysis and artificial intelligence analysis showed moderate consistency in the diagnosis of fundus lesions in diabetic patients. The accuracy of artificial intelligence interpretation is 87.8%.
Objective To observe the expression of N-cadherin in streptozotocin (STZ)-induced diabetic Sprague-Dawley (SD) ratsprime;retinae. Methods Celiac injection with 65 mg/kg STZ was performed on 20 rats to set up the diabetic model, and celiac injection with the same volume citrate buffer was performed on other 20 SD rats as the control. Vascular permeability was detected by Evans blue method. The expression of N-cadherin in both normal and STZ-induced diabetic ratsprime;retinae and trypsinase-digested retinal microvessels were detected by immunohistochemistry method and Western blotting analysis. Results Retinal vascular permeability increased 68%, 91% and 125% 4, 8, and 12 weeks, respectively, after diabetic models was induced (Plt;0.005). In the control group, the expression of N-cadherin was detected in the outer and inner plexiform layer, inner nuclear layer，ganglion cell layer,internal limiting membrane and between retinal endothelial cells and pericytes. However, the expression of N-cadherin significantly decreased in STZ-induced diabetic rats retinae at the 12th week. The results of Western blotting analysis showed that the expression of N-cadherin obviously decreased as the diabetic retinopathy developed. Conclusion The decrease of expression of Ncadherin in the retinae of STZ-induced diabetic rats suggests that N-cadherin may participate in the development of diabetic retinopathy at the early stage. (Chin J Ocul Fundus Dis,2007,23:269-272)