DeepUWF-plus: automatic fundus identification and diagnosis system based on ultrawide-field fundus imaging

Poor eye health is a major public health problem, and the timely detection and diagnosis of fundus abnormalities is important for eye health protection. Traditional imaging models can hinder the comprehensive evaluation of fundus abnormalities due to the use of a narrow field of view. The emerging ultrawide-field (UWF) imaging model surpasses this limitation in a non invasive, wide-view manner and is suitable for fundus observation and screening. Nevertheless, manual screening is labour intensive and subjective, especially in the absence of an ophthalmologist. Therefore, a set of auxiliary screening methods for a fundus screening service using a combination of deep learning and UWF imaging technology, which is designated as DeepUWF-Plus, is proposed. This service includes a subsystem for the screening of fundus, a subsystem for the identification of abnormalities regarding four important fundus locations, and a subsystem for the diagnosis of four retinal diseases that threaten vision. The influence of two-stage and one-stage classification strategies on the prediction performance of the model is experimentally investigated to alleviate severe class imbalance and similarity between classes, and evaluate the effectiveness and reliability of the system. Our experimental results show that DeepUWF-Plus is effective when using the two-stage strategy, especially for identifying signs or symptoms of minor diseases. DeepUWF-Plus can improve the practicality of fundus screening and enable ophthalmologists to provide more comprehensive fundus assessments.