PDF(Pubmed)
Abstract：
Diabetic retinopathy (DR) is the leading cause of visual impairment globally. It occurs due to long-term diabetes with fluctuating blood glucose levels. It has become a significant concern for people in the working age group as it can lead to vision loss in the future. Manual examination of fundus images is time-consuming and requires much effort and expertise to determine the severity of the retinopathy. To diagnose and evaluate the disease, deep learning-based technologies have been used, which analyze blood vessels, microaneurysms, exudates, macula, optic discs, and hemorrhages also used for initial detection and grading of DR. This study examines the fundamentals of diabetes, its prevalence, complications, and treatment strategies that use artificial intelligence methods such as machine learning (ML), deep learning (DL), and federated learning (FL). The research covers future studies, performance assessments, biomarkers, screening methods, and current datasets. Various neural network designs, including recurrent neural networks (RNNs), generative adversarial networks (GANs), and applications of ML, DL, and FL in the processing of fundus images, such as convolutional neural networks (CNNs) and their variations, are thoroughly examined. The potential research methods, such as developing DL models and incorporating heterogeneous data sources, are also outlined. Finally, the challenges and future directions of this research are discussed.
摘要：
糖尿病性视网膜病变(DR)是全球视觉障碍的主要原因。它是由于长期糖尿病和血糖水平波动而发生的。它已经成为工作年龄组的人们的一个重要问题，因为它可能导致未来的视力丧失。眼底图像的手动检查是耗时的并且需要大量的努力和专业知识来确定视网膜病变的严重程度。诊断和评估疾病，基于深度学习的技术已经被使用，分析血管，微动脉瘤,分泌物,黄斑,光盘,和出血也用于DR的初始检测和分级。这项研究检查了糖尿病的基本原理，其患病率,并发症,以及使用机器学习(ML)等人工智能方法的治疗策略，深度学习(DL)，和联邦学习(FL)。这项研究涵盖了未来的研究，绩效评估，生物标志物，筛选方法,和当前数据集。各种神经网络设计，包括递归神经网络(RNN)，生成对抗网络(GAN)，以及ML的应用，DL,和FL在眼底图像处理中，例如卷积神经网络(CNN)及其变体，彻底检查。潜在的研究方法，例如开发DL模型和合并异构数据源，也概述了。最后,讨论了本研究面临的挑战和未来的发展方向。
