In this work, we propose an AI-based method that intends to improve the
conventional retinal disease treatment procedure and help ophthalmologists
increase diagnosis efficiency and accuracy. The proposed method is composed of
a deep neural networks-based (DNN-based) module, including a retinal disease
identifier and clinical description generator, and a DNN visual explanation
module. To train and validate the effectiveness of our DNN-based module, we
propose a large-scale retinal disease image dataset. Also, as ground truth, we
provide a retinal image dataset manually labeled by ophthalmologists to
qualitatively show, the proposed AI-based method is effective. With our
experimental results, we show that the proposed method is quantitatively and
qualitatively effective. Our method is capable of creating meaningful retinal
image descriptions and visual explanations that are clinically relevant.

本研究针对视网膜疾病治疗方案提出一种基于人工智能的方法，旨在帮助眼科医生提高诊断效率和准确性。该方法包括深度神经网络模型、视网膜疾病自动诊断和临床描述生成器、DNN 可视化解释模块，并提供基于眼科医生手动标注的视网膜图像数据集进行训练和验证。实验结果表明，该方法无论是定量还是定性分析都取得了显著的效果，并成功生成了有临床意义的视网膜图像描述和可视化解释。