Patients with DM had a larger FAZ, and patients with more severely damaged retinas had a much larger FAZ. OCT angiography is a new convenient and noninvasive method for studying the FAZ. This novel examination will yield considerable amounts of data that cannot be obtained using previous research methods.

Taken together, the results of the current study suggest that AMSCs may improve the integrity of the BRB in diabetic rats by differentiation into photoreceptor and glial-like cells in the retina and by reducing the blood glucose levels. Furthermore, the data presented herein provide evidence that AMSCs may serve as a promising therapeutic approach for diabetic retinopathy.

Transplantation of autologous ILM is an effective addition to the surgical options for large macular holes after failed surgeries with ILM removal.

AimsTo investigate the efficacy of a bi-modality deep convolutional neural network (DCNN) framework to categorise age-related macular degeneration (AMD) and polypoidal choroidal vasculopathy (PCV) from colour fundus images and optical coherence tomography (OCT) images.MethodsA retrospective cross-sectional study was proposed of patients with AMD or PCV who came to Peking Union Medical College Hospital. Diagnoses of all patients were confirmed by two retinal experts based on diagnostic gold standard for AMD and PCV. Patients with concurrent retinal vascular diseases were excluded. Colour fundus images and spectral domain OCT images were taken from dilated eyes of patients and healthy controls, and anonymised. All images were pre-labelled into normal, dry or wet AMD or PCV. ResNet-50 models were used as the backbone and alternate machine learning models including random forest classifiers were constructed for further comparison. For human-machine comparison, the same testing data set was diagnosed by three retinal experts independently. All images from the same participant were presented only within a single partition subset.ResultsOn a test set of 143 fundus and OCT image pairs from 80 eyes (20 eyes per-group), the bi-modal DCNN demonstrated the best performance, with accuracy 87.4%, sensitivity 88.8% and specificity 95.6%, and a perfect agreement with diagnostic gold standard (Cohen’s κ 0.828), exceeds slightly over the best expert (Human1, Cohen’s κ 0.810). For recognising PCV, the model outperformed the best expert as well.ConclusionA bi-modal DCNN for automated classification of AMD and PCV is accurate and promising in the realm of public health.

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This paper studies automated categorization of age-related macular degeneration (AMD) given a multi-modal input, which consists of a color fundus image and an optical coherence tomography (OCT) image from a specific eye. Previous work uses a traditional method, comprised of feature extraction and classifier training that cannot be optimized jointly. By contrast, we propose a two-stream convolutional neural network (CNN) that is end-to-end. The CNN's fusion layer is tailored to the need of fusing information from the fundus and OCT streams. For generating more multi-modal training instances, we introduce Loose Pair training, where a fundus image and an OCT image are paired based on class labels rather than eyes. Moreover, for a visual interpretation of how the individual modalities make contributions, we extend the class activation mapping technique to the multi-modal scenario. Experiments on a real-world dataset collected from an outpatient clinic justify the viability of our proposal for multi-modal AMD categorization.

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Intraretinal cysts could form in various layers of the outer retina and may result from extension of choroidal inflammation. Subretinal fibrosis may develop from subretinal exudates in VKH patients and may cause substantial visual impairment.