Gautam Anjali, Shanker Ravi
Computer Vision and Biometrics Laboratory, Department of Information Technology, Indian Institute of Information Technology, Allahabad, Prayagraj, Uttar Pradesh, India.
Department of Electronics and Communication Engineering, Indian Institute of Information Technology Ranchi, Ranchi, Jharkhand, India.
Comput Biol Med. 2025 Sep;196(Pt B):110715. doi: 10.1016/j.compbiomed.2025.110715. Epub 2025 Jul 18.
Diabetes is one of the most common diseases worldwide and requires accurate diagnosis. Patients with diabetes are often affected by diabetic retinopathy (DR), which can lead to low vision, vision loss, or blindness. Therefore, a robust computer-aided diagnosis system is needed to provide better treatment to patients. This review mainly focuses on the works related to diagnosing DR from retinal fundus images. A total of 128 research papers have been reviewed from 1986 to 2025. The survey is divided into two parts: one for the grading/classification of DR and the other for DR lesions segmentation. This survey article introduces the details of eye diseases, followed by the background details of DR and different imaging techniques required to diagnose DR, like fundus imaging, multifocal electroretinogram, and optical coherence tomography. Details of well-known DR datasets since 2009 are also provided, including their complete statistical information and potential dataset biases. Furthermore, the approaches used for grading and segmentation tasks from the early 1980s to recent developments are discussed. The reviewed papers are based on traditional and deep learning based methods used in DR diagnosis. In traditional methods, the researchers used image preprocessing, mathematical morphology, fuzzy system, active contour, features extraction methods, evolutionary approaches, and machine learning based classifiers. In deep learning, researchers have used convolutional neural network (CNN), long short-term memory, vision transformer, contrastive learning, federated learning, and Explainable Artificial Intelligence (XAI) based approaches for diagnosis. In this article, we have emphasized almost all the significant work done in diagnosing DR disease, the datasets used, and performance of methods on those datasets. The comparative analysis of the methods is also done to help researchers obtain future directions for further research in the area of medical disease identification, especially DR disease detection. The challenges of AI and its associated ethical implications are also discussed in the article to provide direction for future work.
糖尿病是全球最常见的疾病之一，需要准确诊断。糖尿病患者常受糖尿病视网膜病变（DR）影响，可导致视力低下、视力丧失或失明。因此，需要一个强大的计算机辅助诊断系统为患者提供更好的治疗。本综述主要关注与从视网膜眼底图像诊断DR相关的研究工作。对1986年至2025年的128篇研究论文进行了综述。该调查分为两部分：一部分用于DR的分级/分类，另一部分用于DR病变分割。本文介绍了眼部疾病的细节，随后是DR的背景细节以及诊断DR所需的不同成像技术，如眼底成像、多焦视网膜电图和光学相干断层扫描。还提供了自2009年以来著名的DR数据集的详细信息，包括其完整的统计信息和潜在的数据集偏差。此外，还讨论了从20世纪80年代初到近期发展用于分级和分割任务的方法。所综述的论文基于DR诊断中使用的传统方法和深度学习方法。在传统方法中，研究人员使用了图像预处理、数学形态学、模糊系统、主动轮廓、特征提取方法、进化方法和基于机器学习的分类器。在深度学习中，研究人员使用卷积神经网络（CNN）、长短期记忆、视觉Transformer、对比学习、联邦学习和基于可解释人工智能（XAI）的方法进行诊断。在本文中，我们强调了在诊断DR疾病、所使用的数据集以及这些数据集上方法的性能方面几乎所有的重要工作。还对这些方法进行了比较分析，以帮助研究人员获得医学疾病识别领域，特别是DR疾病检测领域进一步研究的未来方向。本文还讨论了人工智能的挑战及其相关的伦理影响，为未来的工作提供方向。
