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%.
Axon guidance molecules, slit glycoprotein (Slit) and Roundabout receptors (Robo) were firstly identified in the central neural system of Drosophila melanogaster. The Slit-Robo signal plays a crucial role in axon guidance, inflammation, tumor metastasis and angiogenesis, of which the role of Slit2-Robo pathway in angiogenesis has drawn a greater attention and still remains conflicting. Herein, we provide a review on the role of Slit2-Robo pathway in ocular angiogenesis and vascularization of other organs and systems. We hope this review will be the basis of further study on the mechanism of Slit2-Robo signaling on angiogenesis and provide new target for the therapy on ocular vascular disease
Objective To automatically segment diabetic retinal exudation features from deep learning color fundus images. Methods An applied study. The method of this study is based on the U-shaped network model of the Indian Diabetic Retinopathy Image Dataset (IDRID) dataset, introduces deep residual convolution into the encoding and decoding stages, which can effectively extract seepage depth features, solve overfitting and feature interference problems, and improve the model's feature expression ability and lightweight performance. In addition, by introducing an improved context extraction module, the model can capture a wider range of feature information, enhance the perception ability of retinal lesions, and perform excellently in capturing small details and blurred edges. Finally, the introduction of convolutional triple attention mechanism allows the model to automatically learn feature weights, focus on important features, and extract useful information from multiple scales. Accuracy, recall, Dice coefficient, accuracy and sensitivity were used to evaluate the ability of the model to detect and segment the automatic retinal exudation features of diabetic patients in color fundus images. Results After applying this method, the accuracy, recall, dice coefficient, accuracy and sensitivity of the improved model on the IDRID dataset reached 81.56%, 99.54%, 69.32%, 65.36% and 78.33%, respectively. Compared with the original model, the accuracy and Dice index of the improved model are increased by 2.35% , 3.35% respectively. Conclusion The segmentation method based on U-shaped network can automatically detect and segment the retinal exudation features of fundus images of diabetic patients, which is of great significance for assisting doctors to diagnose diseases more accurately.
Objective To investigate the clinical characteristic of ocular fundus complications in systemic lupus erythematosus (SLE). Methods In 25 cases of SLE with the ocular fundus complications, the ocular fundus, the other ocular tissues, general lesions，and antinuclear antibody (ANA ), anti-double-stranded DNA（anti-dsDNA）, complement 3 (C3), complement 4 (C4)and erythrocyte sedimentation rate(ESR) were analyzed retrospectively. Results In the 25 cases, “classic” SLE retinopathy in 15 (25 eyes), retinal vein occlusion (RVO) in 9 (12 eyes), RVO combined with retinal arter y occlusion in 1 (2 eyes), exudative retinal detachment in 1 (2 eyes), vitreous hemorrhage combined with neovascular glaucoma in 1 (1 eye), and optic discedem a except RVO in 3 (6 eyes) were found. Nine cases accompanied with other ocular signs and 21 with general lesions. Positive ANA and anti-dsDNA and elevated ESR in all of the patients, decreasing C3 in 19, and C4in 17 were found.Conclusions SLE can cause serious ocular fundus complications accompanied with other ocular signs. Regular ophthalmic examination should be performed on the patients with SLE to detect and treat the ocular complications promptly. (Chin J Ocul Fundus Dis，2004，20：206-208)
The mineralocorticoid receptor (MR) belongs to the nuclear receptor superfamily and is expressed in the retina and choroid. MR antagonist (MRA) has a long history of application in non-ophthalmic clinical practice. Various cellular and animal models indicated that inappropriate activation of MR participated in pathological angiogenesis, oxidative stress, inflammation, disturbance of ion/water homeostasis and neurodegenerative changes, while the application of MRA can reduce or reverse these pathological processes. After using MRA in central serous chorioretinopathy (CSC) patients, improved visual function, less subretinal fluid and reduced sub-foveal choroidal thickness were observed. Single nucleotide polymorphisms in MR and plasma aldosterone levels were significantly different between chronic CSC patients and CSC patients with spontaneous remission. Novel formulation for sustained-release MRA and the mechanisms involving inflammation may become the new focus of MR study. This review summarizes the research status of MR and MRA in order to provide a reference for future basic research and clinical treatment.