Abstract:Retinal neovascular diseases represent a critical subset of retinal diseases that severely impair vision and can lead to blindness. In recent years, artificial intelligence(AI)has demonstrated breakthrough applications in the medical field, particularly in ophthalmology, leveraging its robust capabilities in image recognition and data analysis. Machine learning and deep learning, as core AI technologies, enable precise feature extraction from vast volumes of medical imaging data and the construction of predictive models, offering novel approaches for the auxiliary diagnosis and prognosis of retinal neovascular diseases. This review synthesizes the latest advancements in AI applications for neovascular retinal diseases, including diabetic retinopathy, retinal vein occlusion, retinopathy of prematurity, and age-related macular degeneration. It further discusses the limitations and challenges in clinical implementation. Through a comprehensive summary and analysis, this review aims to provide insights for advancing AI-driven diagnosis and treatment strategies, ultimately facilitating early detection and predictive management of these vision-threatening diseases.
