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.
目的 探讨视网膜微血管直径与2型糖尿病(DM2)并发症糖尿病视网膜病变(DR)的关系。 方法 选取2009年1月-11月住院确诊DM2患者200例，根据眼底彩色照相结果将患者分为DR组和NDR组，测量视网膜血管直径、测定生化指标及血压，用非条件Logistic回归分析糖尿病视网膜病变发生的危险因素。 结果 V1扩张10 μm时，DM2患者并发DR危险性增加(OR 1.75，95% CI 1.14～3.04，Plt;0.05)；空腹血糖水平增加1 mmol/L，DM2患者并发DR的危险性增加(OR 1.87，95% CI 1.43～2.81，Plt;0.05)； 糖化血红蛋白增加1个单位，DM2患者并发DR的危险性增加(OR 1.08，95% CI 1.02～1.13，Plt;0.05)；DM病程增加1年，DM2患者并发DR的危险性增加(OR 1.41，95% CI 1.18～1.70，Plt;0.05)。 结论 在DM2患者中，视网膜静脉直径大小、空腹血糖水平、糖化血红蛋白水平、糖尿病病程是DR发生的危险因素。
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%.
Peripheral exudative hemorrhagic chorioretinopathy (PEHCR) is a peripheral retinal disease characterized by subretinal hemorrhage and/or subretinal pigment epithelial hemorrhage or exudation. It is often misdiagnosed as age-related macular degeneration, polypoidal chorioretinopathy or choroidal melanoma. With the development of multimodal imaging, PEHCR has different features under different examinations, such as B-scan ultrasound, fluorescein fundus angiography, optical coherence tomography and so on, which contributes to differention from other diseases. Clinical treatments for the disease include intravitreal injection of retinal photocoagulation therapy, anti-vascular endothelial growth factor, pars plana vitrectomyand so on, but there is still no universal consensus. In order to gain a deeper understanding of the clinical features, treatment options and prognosis of PEHCR, minimize missed diagnoses and misdiagnoses, and improve treatment efficiency, further research is required.
Objective To observe the ocular fundus features and consistency of classification of diabetic retinopathy (DR) by ultra-wide-field fluorescein angiography (UWFA) and the simulated early treatment diabetic retinopathy study (ETDRS) 7 standard field (7SF) imaging. Methods This is a retrospective clinical description study. Ninety-six eyes of 55 DR patients were included. The ages ranged from 25 to 73 years, with a mean age of (41.34±15.07) years. UWFA examination (British Optos 200Tx imaging system) using the protocol for obtaining 7SF images as described in the ETDRS, 7 circular regions with a range of 30 degrees are spliced as 7SF templates to determine the observation range. This template was then overlaid on the UWFA image to identify the potential viewable area of 7SF. And the visualized area of the retina, retinal non-perfusion (NP) area, retinal neovascularization (NV) area, and pan-retinal photocoagulation (PRP) area of UWFA and 7SF were quantified by a retinal specialist. Results UWFA imaging and 7SF imaging have a high degree of consistency in judging DR classification (kappa=0.851,P=0.000). The retinal visual area, NP area, NV area and PRP area of the UWFA imaging were 3.16, 3.38, 2.22 and 3.15 times more comparing with the simulated 7SF imaging (t=213.430, 45.013, 22.644, 142.665;P=0.000, 0.000, 0.003, 0.000). The lesions of 8 eyes were found outside the range of simulated 7SF imaging, including peripheral NP in 5 eyes, NV areas in 3 eyes, respectively. Conclusion UWFA imaging and simulated 7SF imaging are consistent to judge DR classification, but UWFA can find more peripheral retinal lesions.