Ultra Wide

Ultra-widefield (UWF) imaging, capturing a significantly broader view of the retina than traditional methods, is revolutionizing ophthalmic diagnostics. Current research focuses on leveraging deep learning, particularly convolutional neural networks (CNNs) like ResNet and EfficientNet, and generative adversarial networks (GANs), to automate analysis of UWF images for detecting and classifying various retinal diseases, including diabetic retinopathy and myopia. This involves developing algorithms for image quality assessment, disease detection, and even synthesizing alternative imaging modalities (e.g., fluorescein angiography from fundus photography) to reduce the need for invasive procedures. These advancements promise to improve the efficiency and accuracy of retinal disease diagnosis, leading to earlier interventions and better patient outcomes.