ObjectiveTo observe the blood perfusion changes of peripapillary and macular vessels in patients with nonarteritic anterior ischaemic optic neuropathy (NAION).MethodsRetrospective cohort study. Thirty-six eyes (19 affected eyes and 17 fellow eyes) of 19 patients with NAION diagnosed in People’s Hospital of Wuhan University from November 2017 to January 2019 were included in this study. There were 10 males and 9 females, with the mean age of 55.05±7.11 years. Forty eyes of 20 normal subjects matched with NAION patients were included as controls. BCVA, fundus color photography, SD-OCT and OCT angiography were performed in normal controls and repeated in NAION affected eyes at 1-2 weeks, 1-2 months, 3-5 months intervals. OCT quantitative measurements: average retinal nerve fiber layer thickness (aRNFL) of the disc and its superior values (sRNFL) and the inferior values (iRNFL), average ganglion cell complex thickness (aGCC) in macular region and its superior values (sGCC) and the inferior values (iGCC). OCTA quantitative measurements: average radial peripapillary capillary density (aRPC) and its superior values (sRPC) and the inferior values (iRPC), average vascular density of superficial retina (aSVD) in macular region and its superior values (sSVD) and the inferior values (iSVD), average vascular density of deep layer retina (aDVD), areas of foveal avascular zone (FAZ). The differences of OCT and OCTA quantitative measurements between NAION eyes and the fellow eyes and normal controls were comparatively analyzed. Independent sample t test, paired sample t test or nonparametric rank sum test were performed for comparison among three groups. Pearson or Spearman correlation analysis were used to analyze the correlation between RNFL and RPC, GCC and SVD, RNFL and GCC, RPC and SVD.ResultsAt baseline, the aRNFL, aRPC and aDVD of NAION patients were significantly higher than those of normal controls. Compared with the fellow eyes, the aRNFL increased significantly and the aRPC decreased significantly in NAION affected eyes. The overall differences of aRNFL, aRPC, aGCC and aSVD at four intervals within NAION affected eyes were statistically significant (P＜0.05). The average sRNFL, sRPC, sGCC and sSVD at 1-2 months interval were significantly lower than the average iRNFL, iRPC, iGCC and iSVD (P＜0.05). Correlation analysis: at 1-2 months interval, aGCC was positively correlated with aSVD (r=0.482, P=0.037); at 3-5 months interval, aRNFL was positively correlated with aRPC (r=0.631, P=0.037).ConclusionThere is a sectorial reduction of vascular density of peripapillary RPC and macular SVD with the disease progression of NAION.
ObjectiveTo assess changes of blood flow density of idiopathic choroidal neovascularization (ICNV) treated with intravitreal anti-vascular endothelial growth factor (anti-VEGF).MethodsRetrospective case analysis. Sixteen eyes of 16 patients with ICNV diagnosed with FFA and OCT were included in this study. Among them, 12 were female and 4 were male. The mean age was 33.94±9.83 years. The mean course of diseases was 5.13±4.44 weeks. The BCVA, indirect ophthalmoscope, OCT and OCT angiography (OCTA) were performed at the first diagnosis in all patients. The BCVA was converted to logMAR. The macular fovea retinal thickness (CMT) was measured by OCT, and the selected area of CNV (CSA) and flow area of CNV (CFA) were measured by OCTA. The mean logMAR BCVA, CMT, CSA and CFA were 0.336±0.163, 268.500±57.927 μm, 0.651±0.521 mm2, 0.327±0.278 mm2 , respectively. All patients were treated with intravitreal ranibizumab (IVR, 10 mg/ml, 0.05 ml). Follow-up results including the BCVA, fundus color photography, OCT and OCTA were obtained 1 month after treatment. To compare the changes of BCVA, CMT, CSA, CFA of ICNV treated with anti-VEGF. Pearson method was used to analyze the correlation between logMAR BCVA and CMT, CSA and CFA before and after the treatment.ResultsOne month after treatment, the average logMAR BCVA, CMT, CSA and CFA were 0.176±0.111, 232.500±18.910 μm, 0.420±0.439 mm2, 0.215±0.274 mm2. The mean logMAR BCVA (t=5.471, P＜0.001), CMT (t=2.527, P=0.023), CSA (t=4.039, P=0.001), CFA (t=4.214, P=0.001) significantly decreased at 1 month after injection compared to baseline, and the difference had statistical significance. The results of correlation analysis showed that the post-logMAR BCVA was moderately positively correlated with pre-CSA and post-CSA (r=0.553, 0.560; P=0.026, 0.024), and strongly correlated with pre-CFA and post-CFA (r=0.669, 0.606; P=0.005, 0.013), but not correlated with pre-CMT and post-CMT (r=0.553, 0.560; P=0.026, 0.024).ConclusionThe blood flow density of ICNV measured by OCTA were significantly decreased in the treatment of anti-VEGF drugs.
ObjectiveTo observe the imaging features of cystoid macular edema (CME) in multicolor imaging (MC), and to evaluate the value of MC in the diagnosis of CME.MethodsDescriptive case series study. From August 2017 to June 2018, 42 eyes of 37 patients with CME diagnosed in the people's Hospital of Wuhan University were included in the study. Among them, there were 24 males and 13 females, with an average age of 48.51±10.29 years. There were 14 eyes with diabetic retinopathy, 14 eyes with central retinal vein occlusion, 8 eyes with branch retinal vein occlusion, 4 eyes with uveitis, and 2 eyes with Eales disease. The macular color fundus photography (CFP) was performed with Visucam 200 non-mydriatic fundus camera of Zeiss company in Germany. MC, frequnce domainoptical OCT (SD-OCT) and FFA were examined by Spectralis HRA2 + OCT of Heidelberg company in Germany. According to the MC standard method, five images, including 488 nm blue reflection (BR), 515 nm green reflection (GR), 820 nm infrared reflection (IR) imaging and standard MC and blue-green enhancement (BG), were obtained at the same time. Compared with SD-OCT, CFP and MC images were scored. Friedman M test and Wilcoxon signed rank test were used for statistical analysis.ResultsThe standard MC and BG images showed blue-green uplift area or petal-shaped appearance, surrounded by green reflection areas with clear boundaries. BR image can be seen in the low reflexes area. On the GR image, there were patches or cystic low reflection areas, surrounded by a slightly high reflection. On the IR image, patches or cystoid high reflexes can be seen, surrounded by low reflection dark areas with clear boundaries. The average scores of CFP, standard MC, GB, IR, GR and BR were 1.20±0.94, 3.05±0.99, 2.90±1.04, 2.55±1.27, 2.00±0.94, 0.51±0.85 respectively, and the differences were statistically significant (χ2= 151.61, P=0.000). The score of CFP were significantly lower than that of standard MC (Z=－5.421), BG (Z=－5.354), IR (Z=－4.714), GR (Z=－4.438) and higher than that of BR (Z=－3.435). The differences were statistically significant (P=0.000, 0.000, 0.000, 0.000, 0.001).ConclusionsThe quality of MC imaging is better than that of CFP. Combined with SD-OCT, it can be used as an assistant method to diagnose CME.
ObjectiveTo observe and preliminarily discuss the distribution characteristics of the non-perfusion area (NP) of the retina in different stages of diabetic retinopathy (DR) and its changes with the progression of DR. MethodsA retrospective clinical study. From October 2018 to December 2020, 118 cases of 175 eyes of DR patients diagnosed in Eye Center of Renmin Hospital of Wuhan University were included in the study. Among them, there were 64 males with 93 eyes and 54 females with 82 eyes; the average age was 56.61±8.99 years old. There were 95 eyes of non-proliferative DR (NPDR), of which 25, 47, and 23 eyes were mild, moderate, and severe; 80 eyes were proliferative DR (PDR). Ultra-wide-angle fluorescein fundus angiography was performed with the British Optos 200Tx imaging system, and the fundus image was divided into posterior, middle, and distal parts with Image J software, and the ischemic index (ISI) was calculated. The difference of the retina in different DR staging groups and the difference of ISI were compared in the same area. The Kruskal-Wallis test was used to compare the ISI between the different DR staging groups and the Kruskal-Wallis one-way analysis of variance was used for the pairwise comparison between the groups. ResultsThe ISI of the posterior pole of the eyes in the moderate NPDR group, severe NPDR group, and PDR group were significantly greater than that in the distal periphery, and the difference was statistically significant (χ2=6.551, 3.540, 6.614; P=0.000, 0.002, 0.000). In severe NPDR group and PDR group, the ISI of the middle and peripheral parts of the eyes was significantly greater than that of the distal parts, and the difference was statistically significant (χ2=3.027, 3.429; P=0.015, 0.004). In the moderate NPDR group, there was no significant difference in ISI between the peripheral and distal parts of the eye (χ2=2.597, P=0.057). The ISI of the posterior pole of the eyes in the moderate NPDR group and the PDR group was significantly greater than that in the middle periphery, and the difference was statistically significant (χ2=3.955, 3.184; P=0.000, 0.009). In the severe NPDR group, there was no significant difference in ISI between the posterior pole and the middle periphery of the eye (χ2=0.514, P=1.000). Compared with the mild NPDR group and the moderate NPDR group, the ISI of the whole retina, posterior pole, middle and distal parts of the PDR group was larger, and the difference was statistically significant (χ2=－7.064, －6.349,－6.999, －5.869, －6.695, －6.723, －3.459, －4.098; P=0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.003, 0.000). ConclusionThe NP of the eyes with different DR stages is mainly distributed in the posterior pole and the middle periphery. The higher the severity of DR, the greater the NP in the posterior and middle periphery.
ObjectiveTo build a small-sample ultra-widefield fundus images (UWFI) multi-disease classification artificial intelligence model, and initially explore the ability of artificial intelligence to classify UWFI multi-disease tasks. MethodsA retrospective study. From 2016 to 2021, 1 608 images from 1 123 patients who attended the Eye Center of the Renmin Hospital of Wuhan University and underwent UWFI examination were used for UWFI multi-disease classification artificial intelligence model construction. Among them, 320, 330, 319, 268, and 371 images were used for diabetic retinopathy (DR), retinal vein occlusion (RVO), pathological myopia (PM), retinal detachment (RD), and normal fundus images, respectively. 135 images from 106 patients at the Tianjin Medical University Eye Hospital were used as the external test set. EfficientNet-B7 was selected as the backbone network for classification analysis of the included UWFI images. The performance of the UWFI multi-task classification model was assessed using the receiver operating characteristic curve, area under the curve (AUC), sensitivity, specificity, and accuracy. All data were expressed using numerical values and 95% confidence intervals (CI). The datasets were trained on the network models ResNet50 and ResNet101 and tested on an external test set to compare and observe the performance of EfficientNet with the 2 models mentioned above. ResultsThe overall classification accuracy of the UWFI multi-disease classification artificial intelligence model on the internal and external test sets was 92.57% (95%CI 91.13%-92.92%) and 88.89% (95%CI 88.11%-90.02%), respectively. These were 96.62% and 92.59% for normal fundus, 95.95% and 95.56% for DR, 96.62% and 98.52% for RVO, 98.65% and 97.04% for PM, and 97.30% and 94.07% for RD, respectively. The mean AUC on the internal and external test sets was 0.993 and 0.983, respectively, with 0.994 and 0.939 for normal fundus, 0.999 and 0.995 for DR, 0.985 and 1.000 for RVO, 0.991 and 0.993 for PM and 0.995 and 0.990 for RD, respectively. EfficientNet performed better than the ResNet50 and ResNet101 models on both the internal and external test sets. ConclusionThe preliminary UWFI multi-disease classification artificial intelligence model using small samples constructed in this study is able to achieve a high accuracy rate, and the model may have some value in assisting clinical screening and diagnosis.