Artificial intelligence (AI)-based diagnostic tools have been accepted in ophthalmology. The use of retinal images, such as fundus photographs, is a promising approach for the development of AI-based diagnostic platforms. Retinal pathologies usually occur in a broad spectrum of eye diseases, including neovascular or dry age-related macular degeneration, epiretinal membrane, rhegmatogenous retinal detachment, retinitis pigmentosa, macular hole, retinal vein occlusions, and diabetic retinopathy. Here, we report a fundus image-based AI model for differential diagnosis of retinal diseases. We classified retinal images with three convolutional neural network models: ResNet50, VGG19, and Inception v3. Furthermore, the performance of several dense (fully connected) layers was compared. The prediction accuracy for diagnosis of nine classes of eight retinal diseases and normal control was 87.42% in the ResNet50 model, which added a dense layer with 128 nodes. Furthermore, our AI tool augments ophthalmologist’s performance in the diagnosis of retinal disease. These results suggested that the fundus image-based AI tool is applicable for the medical diagnosis process of retinal diseases.

The use of deep-learning-based artificial intelligence (AI) is emerging in ophthalmology, with AI-mediated differential diagnosis of neovascular age-related macular degeneration (AMD) and dry AMD a promising methodology for precise treatment strategies and prognosis. Here, we developed deep learning algorithms and predicted diseases using 399 images of fundus. Based on feature extraction and classification with fully connected layers, we applied the Visual Geometry Group with 16 layers (VGG16) model of convolutional neural networks to classify new images. Image-data augmentation in our model was performed using Keras ImageDataGenerator, and the leave-one-out procedure was used for model cross-validation. The prediction and validation results obtained using the AI AMD diagnosis model showed relevant performance and suitability as well as better diagnostic accuracy than manual review by first-year residents. These results suggest the efficacy of this tool for early differential diagnosis of AMD in situations involving shortages of ophthalmology specialists and other medical devices.

Multiple sclerosis (MS) is an inflammatory disease in which myelin in the spinal cord is damaged. C-C chemokine receptor type 5 (CCR5) is implicated in immune cell migration and cytokine release in central nervous system (CNS). We investigated whether CCR5 plays a role in MS progression using a murine model, experimental autoimmune encephalomyelitis (EAE), in CCR5 deficient (CCR5-/-) mice. CCR5-/- and CCR5+/+ (wild-type) mice were immunized with myelin oligodendrocyte glycoprotein 35-55 (MOG35-55) followed by pertussis toxin, after which EAE paralysis was scored for 28 days. We found that clinical scoring and EAE neuropathology were lower in CCR5-/- mice than CCR5+/+ mice. Immune cells (CD3+, CD4+, CD8+, B cell, NK cell and macrophages) infiltration and astrocytes/microglial activation were attenuated in CCR5-/- mice. Moreover, levels of IL-1β, TNF-α, IFN-γ and MCP-1 cytokine levels were decreased in CCR5-/- mice spinal cord. Myelin basic protein (MBP) and CNPase were increased while NG2 and O4 were decreased in CCR5-/- mice, indicating that demyelination was suppressed by CCR5 gene deletion. These findings suggest that CCR5 is likely participating in demyelination in the spinal cord the MS development, and that it could serve as an effective therapeutic target for the treatment of MS.