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Background. Vitreous floater is a physically common phenomenon with aging and is related to visual impairment and decrease of quality of life. Nd:YAG vitreolysis is supposed to be an option for resolving floaters, but its clinical efficacy is undefined. We aimed to evaluate the efficacy of Nd:YAG vitreolysis in treating floater semiquantifiably by determining changes of floater areas on infrared fundus photography (IR). Methods. Patients with floaters and those who underwent Nd:YAG vitreolysis were retrospectively summarized from June 2015 to Nov 2017. Intraocular pressure, visual acuity, visual function questionnaire (VFQ-25) scores, and floater areas calculated using Image J software were recorded preoperatively and 6 months after YAG lasers. Results. 50 patients (25 female/25 male, with an average age of 60.34 years) with 55 eyes (29 OD and 26 OS) presenting floaters and underwent YAG vitreolysis treatment were included. Severe symptoms were reported in 17 eyes, moderate in 21 and mild in 17 eyes. No severe Nd:YAG vitreolysis procedure-related complications occurred in all patients except one mild retinal injury. There were no significant changes in intraocular pressure and visual acuity after the laser treatment. 43 eyes had improved symptoms; in 8, floaters had disappeared; and 4 had no changes according to VFQ-25 scores. The median of shadow areas of floaters before operation was 1.41 (0.29–12.85) cm2, which decreased to 0.12 (0–2.77) cm2 after the operations (t=5.849,  P=0.001). The mean VFQ-25 scores increased to 88.54 ± 12.74 from the baseline 71.44 ± 12.77 (t=11.82,  P=0.001). Pearson correlation analysis showed that the shadow areas of floaters were negatively correlated to VFQ-25 scores before (r=−0.73,  P=0.001) and after (r=−0.72,  P=0.001) treatments. Conclusion. Nd:YAG vitreolysis was effective and safe in alleviating the visual symptoms induced by floaters. Quantification of floater shadow areas on infrared fundus photography could serve as an objective index for assessing treatment efficacy of Nd:YAG vitreolysis.

ObjectiveTo evaluate the etiologies for dense vitreous hemorrhage in adults with non-traumatic and reveal management of early vitrectomy for the disease.MethodsStudy included 105 eyes from 105 patients. Outcome measures were etiologies of vitreous hemorrhage, formation of retinal and/or disk neovascular membrane (NVM), incidence of retinal tear and detachment, visual acuity (VA) and postoperative complications.ResultsMean time between presentation and surgery was 7.1 days. The most common etiologies were retinal vein occlusion (RVO) (58.1%), retinal tear (22.9%) and retinal vasculitis (10.4%). Most RVO (77.0%) and retinal vasculitis (72.7%) eyes were associated with retinal and/or disk NVM. Retinal tear and retinal detachment was found in 24 and 48 eyes, respectively. VA improved significantly from 1/70 to 0.6 following vitrectomy. The most common postoperative complication was cataract (28.6%).ConclusionRVO, retinal tear and retinal vasculitis were the most common causes of dense vitreous hemorrhage. Early vitrectomy has a good outcome with acceptable complication rates in this setting.

Background Myopic maculopathy (MM) has become a major cause of visual impairment and blindness worldwide, especially in East Asian countries. Deep learning approaches such as deep convolutional neural networks (DCNN) have been successfully applied to identify some common retinal diseases and show great potential for the intelligent analysis of MM. This study aimed to build a reliable approach for automated detection of MM from retinal fundus images using DCNN models. Methods A dual-stream DCNN (DCNN-DS) model that perceives features from both original images and corresponding processed images by color histogram distribution optimization method was designed for classification of no MM, tessellated fundus (TF), and pathologic myopia (PM). A total of 36,515 gradable images from four hospitals were used for DCNN model development, and 14,986 gradable images from the other two hospitals for external testing. We also compared the performance of the DCNN-DS model and four ophthalmologists on 3000 randomly sampled fundus images. Results The DCNN-DS model achieved sensitivities of 93.3% and 91.0%, specificities of 99.6% and 98.7%, areas under the receiver operating characteristic curves (AUC) of 0.998 and 0.994 for detecting PM, whereas sensitivities of 98.8% and 92.8%, specificities of 95.6% and 94.1%, AUCs of 0.986 and 0.970 for detecting TF in two external testing datasets. In the sampled testing dataset, the sensitivities of four ophthalmologists ranged from 88.3% to 95.8% and 81.1% to 89.1%, and the specificities ranged from 95.9% to 99.2% and 77.8% to 97.3% for detecting PM and TF, respectively. Meanwhile, the DCNN-DS model achieved sensitivities of 90.8% and 97.9% and specificities of 99.1% and 94.0% for detecting PM and TF, respectively. Conclusions The proposed DCNN-DS approach demonstrated reliable performance with high sensitivity, specificity, and AUC to classify different MM levels on fundus photographs sourced from clinics. It can help identify MM automatically among the large myopic groups and show great potential for real-life applications.

Purpose Diabetic retinopathy (DR) is one of the most serious complications of diabetes, which is a kind of fundus lesion with specific changes. Early diagnosis of DR can effectively reduce the visual damage caused by DR. Due to the variety and different morphology of DR lesions, automatic classification of fundus images in mass screening can greatly save clinicians' diagnosis time. To alleviate these problems, in this paper, we propose a novel framework—graph attentional convolutional neural network (GACNN). Methods and Materials The network consists of convolutional neural network (CNN) and graph convolutional network (GCN). The global and spatial features of fundus images are extracted by using CNN and GCN, and attention mechanism is introduced to enhance the adaptability of GCN to topology map. We adopt semi‐supervised method for classification, which greatly improves the generalization ability of the network. Results In order to verify the effectiveness of the network, we conducted comparative experiments and ablation experiments. We use confusion matrix, precision, recall, kappa score, and accuracy as evaluation indexes. With the increase of the labeling rates, the classification accuracy is higher. Particularly, when the labeling rate is set to 100%, the classification accuracy of GACNN reaches 93.35%. Compared with DenseNet121, the accuracy rate is improved by 6.24%. Conclusions Semi‐supervised classification based on attention mechanism can effectively improve the classification performance of the model, and attain preferable results in classification indexes such as accuracy and recall. GACNN provides a feasible classification scheme for fundus images, which effectively reduces the screening human resources.

Introduction. This meta-analysis aimed to compare the therapeutic effect and safety of intravitreal conbercept (IVC) versus intravitreal ranibizumab (IVR) in treatment of diabetic macular edema (DME). Methods. Relevant studies were identified through systemic searches of PubMed, Embase, Cochrane Library, Ovid, CNKI, and Wanfang database up to 28 February 2019. Changes in central retinal thickness (CRT) in μm and best-corrected visual acuity (BCVA) in logMAR equivalents at 1, 3, and 6 months after initial treatment were performed by pooled analysis. Adverse events (AEs) were evaluated. Results. Eight articles involving 588 patients with DME were identified for this meta-analysis. The results showed that IVC significantly improved BCVA compared with IVR at 6 mo (SMD = −0.74 95% CI: −1.28 to −0.2; p=0.029) in patients with DME. IVC was superior to IVR in reducing central retinal thickness (CRT at 1 mo (p<0.0001), 3 mo (p=0.025), and 6 mo (p=0.019)) from baseline with statistical significance. For AEs, the pooled results showed that no significant difference in the risk of intraocular pressure increased (OR = 1.71; 95% CI: 0.55 to 5.25; p=0.352) or conjunctival hemorrhage (OR = 0.89; 95% CI: 0.34 to 2.34; p=0.65) between two groups. Conclusions. This meta-analysis showed that IVC trended to be more effective than IVR in terms of functional and anatomic outcomes for treating DME.

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Introduction To compare the outcome of two different transscleral fixation approaches for posterior chamber intraocular lens (IOL) implantation, a two-point fixation of the Sensar (Allergan) or CZ70BD (Alcon) IOL and a four-point fixation of the Akreos Adapt (Bausch & Lomb) foldable IOL, for treatment of subluxated lenses in Marfan syndrome (MFS). Methods Fifty-three eyes of 33 consecutive patients with subluxated lenses secondary to MFS were studied. Eighteen patients with MFS (30 eyes) received two-point fixation of the Sensar (16 patients, 26 eyes) or CZ70BD (2 patients, 4 eyes) IOL, and 15 patients with MFS (23 eyes) received four-point fixation of the Akreos Adapt IOL. Preoperative and postoperative ophthalmologic examinations were performed. A primary outcome measure of postoperative complication was studied. Results The mean preoperative best corrected visual acuity (BCVA) in the two-point group was 0.68 ± 0.38 logarithm of the minimum angle of resolution (logMAR), and it improved to 0.30 ± 0.32 logMAR at the final follow-up ( p < 0.05). The mean preoperative BCVA in the four-point group was 0.68 ± 0.45 logMAR, and it improved to 0.28 ± 0.28 logMAR at the final follow-up ( p < 0.05). The BCVA results did not differ significantly between groups. The intraocular pressure was increased at the final follow-up in the two-point group ( p < 0.05). Transscleral two-point fixation of IOL has relatively high incidences of pupillary capture of the IOL. Conclusion The closed continuous-loop transscleral four-point fixation of the Akreos Adapt foldable IOL is more suitable than two-point fixation of a two-haptic IOL in treating subluxated lenses due to MFS.