Sedighin Farnaz, Monemian Maryam, Zojaji Zahra, Montazerolghaem Ahmadreza, Asadinia Mohammad Amin, Mirghaderi Seyed Mojtaba, Esfahani Seyed Amin Naji, Kazemi Mohammad, Mokhtari Reza, Mohammadi Maryam, Ramezani Mohadese, Tajmirriahi Mahnoosh, Rabbani Hossein
Medical Image and Signal Processing Research Center, Department of Bioelectrics and Biomedical Engineering, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
Faculty of Computer Engineering, University of Isfahan, Isfahan, Iran.
J Med Signals Sens. 2024 Jan 23;15:3. doi: 10.4103/jmss.jmss_47_24. eCollection 2025.
Computer-aided diagnosis (CAD) methods have become of great interest for diagnosing macular diseases over the past few decades. Artificial intelligence (AI)-based CADs offer several benefits, including speed, objectivity, and thoroughness. They are utilized as an assistance system in various ways, such as highlighting relevant disease indicators to doctors, providing diagnosis suggestions, and presenting similar past cases for comparison.
Much specifically, retinal AI-CADs have been developed to assist ophthalmologists in analyzing optical coherence tomography (OCT) images and making retinal diagnostics simpler and more accurate than before. Retinal AI-CAD technology could provide a new insight for the health care of humans who do not have access to a specialist doctor. AI-based classification methods are critical tools in developing improved retinal AI-CAD technology. The Isfahan AI-2023 challenge has organized a competition to provide objective formal evaluations of alternative tools in this area. In this study, we describe the challenge and those methods that had the most successful algorithms.
A dataset of OCT images, acquired from normal subjects, patients with diabetic macular edema, and patients with other macular disorders, was provided in a documented format. The dataset, including the labeled training set and unlabeled test set, was made accessible to the participants. The aim of this challenge was to maximize the performance measures for the test labels. Researchers tested their algorithms and competed for the best classification results.
The competition is organized to evaluate the current AI-based classification methods in macular pathology detection. We received several submissions to our posted datasets that indicate the growing interest in AI-CAD technology. The results demonstrated that deep learning-based methods can learn essential features of pathologic images, but much care has to be taken in choosing and adapting appropriate models for imbalanced small datasets.
在过去几十年中，计算机辅助诊断（CAD）方法已成为诊断黄斑疾病的研究热点。基于人工智能（AI）的CAD具有诸多优势，包括速度快、客观性强和全面性好。它们以多种方式作为辅助系统使用，例如向医生突出显示相关疾病指标、提供诊断建议以及展示过去的相似病例以供比较。
具体而言，视网膜AI - CAD已被开发出来，以协助眼科医生分析光学相干断层扫描（OCT）图像，并使视网膜诊断比以往更简单、更准确。视网膜AI - CAD技术可以为无法获得专科医生诊治的人群的医疗保健提供新的视角。基于AI的分类方法是开发改进型视网膜AI - CAD技术的关键工具。伊斯法罕AI - 2023挑战赛组织了一场竞赛，旨在对该领域的替代工具进行客观的正式评估。在本研究中，我们描述了该挑战赛以及那些拥有最成功算法的方法。
以文档形式提供了一个OCT图像数据集，该数据集来自正常受试者、糖尿病性黄斑水肿患者和其他黄斑疾病患者。该数据集包括带标签的训练集和无标签的测试集，并提供给了参与者。本次挑战赛的目标是最大化测试标签的性能指标。研究人员测试了他们的算法，并竞争最佳分类结果。
组织此次竞赛是为了评估当前基于AI的分类方法在黄斑病变检测中的性能。我们收到了针对我们发布的数据集的多份提交结果，这表明人们对AI - CAD技术的兴趣日益浓厚。结果表明，基于深度学习的方法可以学习病理图像的基本特征，但在为不平衡的小数据集选择和适配合适的模型时必须格外小心。
