糖尿病视网膜病变(DR)在糖尿病管理中提出了重大挑战,其进展通常无症状,直到晚期。这强调了迫切需要具有成本效益和可靠的筛查方法。因此,人工智能工具的集成为有效解决这一需求提供了一个有希望的途径。我们概述了使用AI进行DR筛查的最新结果和技术的现状,同时还确定了研究中的差距,以备将来探索。通过综合现有数据库和精确定位需要进一步调查的区域,本文旨在指导未来糖尿病视网膜病变自动筛查领域的研究方向。详细介绍为自动筛查糖尿病视网膜病变而设计的深度学习方法的文章数量不断增加,尤其是到2021年。研究人员利用各种数据库,主要关注IDRiD数据集。该数据集包括在位于印度的眼科诊所捕获的彩色眼底图像。它包括516个图像,描绘了糖尿病性视网膜病变和糖尿病性黄斑水肿的各个阶段。每篇论文都集中在各种DR标志上。然而,很大一部分作者主要专注于检测渗出物,这仍然不足以评估这种疾病的整体存在。已经采用各种AI方法来识别DR体征。在选定的论文中,4.7%采用检测方法,46.5%采用分类技术,41.9%依赖细分,7%的人选择了分类和分割相结合。从采用预处理技术的80%的文章中计算的度量表明,这种方法在提高结果质量方面具有显着的优势。此外,多种深度学习技术,从分类开始,检测然后分割。研究人员主要使用YOLO进行检测,用于分类的ViT和用于分割的U-Net。关于糖尿病视网膜病变筛查AI模型不断发展的另一个观点是,越来越多地采用卷积神经网络进行分类任务,并采用U-Net架构进行分割;但是,研究界越来越意识到这些技术,虽然单独强大,集成时可以更有效。这种整合不仅有望诊断DR,而且还能准确地对其不同阶段进行分类,从而实现更量身定制的治疗策略。尽管有这种潜力,用于DR筛查的AI模型的开发充满挑战。其中最主要的是难以获得高质量,训练模型有效执行所需的标记数据。数据的这种稀缺性对实现稳健的性能造成了重大障碍,并且可能阻碍开发精确筛查系统的进展。此外,管理这些模型的复杂性,特别是深度神经网络,提出了自己的挑战。此外,解释这些模型的输出并确保其在现实临床环境中的可靠性仍然是人们关注的问题.此外,训练和使这些模型适应特定数据集的迭代过程可能是耗时且资源密集的。这些挑战强调了开发用于DR筛查的有效AI模型的多面性。解决这些障碍需要研究人员的共同努力,临床医生,和技术人员创新新方法并克服现有限制。通过这样做,AI的全部潜力可能改变DR筛查并改善患者预后.
Diabetic retinopathy (
DR) poses a significant challenge in diabetes management, with its progression often asymptomatic until advanced stages. This underscores the urgent need for cost-effective and reliable screening methods. Consequently, the integration of artificial intelligence (AI) tools presents a promising avenue to address this need effectively. We provide an overview of the current state of the art results and techniques in DR screening using AI, while also identifying gaps in research for future exploration. By synthesizing existing database and pinpointing areas requiring further investigation, this paper seeks to guide the direction of future research in the field of automatic diabetic retinopathy screening. There has been a continuous rise in the number of articles detailing deep learning (DL) methods designed for the automatic screening of diabetic retinopathy especially by the year 2021. Researchers utilized various databases, with a primary focus on the IDRiD dataset. This dataset consists of color fundus images captured at an ophthalmological clinic situated in India. It comprises 516 images that depict various stages of
DR and diabetic macular edema. Each of the chosen papers concentrates on various
DR signs. Nevertheless, a significant portion primarily focused on detecting exudates, which remains insufficient to assess the overall presence of this disease. Various AI methods have been employed to identify
DR signs. Among the chosen papers, 4.7 % utilized detection methods, 46.5 % employed classification techniques, 41.9 % relied on segmentation, and 7 % opted for a combination of classification and segmentation. Metrics calculated from 80 % of the articles employing preprocessing techniques demonstrated the significant benefits of this approach in enhancing results quality. In addition, multiple DL techniques, starting by classification, detection then segmentation. Researchers used mostly YOLO for detection, ViT for classification, and U-Net for segmentation. Another perspective on the evolving landscape of AI models for diabetic retinopathy screening lies in the increasing adoption of Convolutional Neural Networks for classification tasks and U-Net architectures for segmentation purposes; however, there is a growing realization within the research community that these techniques, while powerful individually, can be even more effective when integrated. This integration holds promise for not only diagnosing DR, but also accurately classifying its different stages, thereby enabling more tailored treatment strategies. Despite this potential, the development of AI models for DR screening is fraught with challenges. Chief among these is the difficulty in obtaining the high-quality, labeled data necessary for training models to perform effectively. This scarcity of data poses significant barriers to achieving robust performance and can hinder progress in developing accurate screening systems. Moreover, managing the complexity of these models, particularly deep neural networks, presents its own set of challenges. Additionally, interpreting the outputs of these models and ensuring their reliability in real-world clinical settings remain ongoing concerns. Furthermore, the iterative process of training and adapting these models to specific datasets can be time-consuming and resource-intensive. These challenges underscore the multifaceted nature of developing effective AI models for DR screening. Addressing these obstacles requires concerted efforts from researchers, clinicians, and technologists to develop new approaches and overcome existing limitations. By doing so, a full potential of AI may transform
DR screening and improve patient outcomes.