Zou Yanli, Wang Yujuan, Kong Xiangbin, Chen Tingting, Chen Jiangna, Li Yiqun
School of Basic Medical Sciences, Southern Medical University, Guangzhou 510000, China.
Department of Ophthalmology, Foshan Hospital Affiliated to Southern Medical University, Foshan 528000, China.
Diagnostics (Basel). 2023 Sep 18;13(18):2985. doi: 10.3390/diagnostics13182985.
Retinal diseases are a serious and widespread ophthalmic disease that seriously affects patients' vision and quality of life. With the aging of the population and the change in lifestyle, the incidence rate of retinal diseases has increased year by year. However, traditional diagnostic methods often require experienced doctors to analyze and judge fundus images, which carries the risk of subjectivity and misdiagnosis. This paper will analyze an intelligent medical system based on focal retinal image-aided diagnosis and use a convolutional neural network (CNN) to recognize, classify, and detect hard exudates (HEs) in fundus images (FIs). The research results indicate that under the same other conditions, the accuracy, recall, and precision of the system in diagnosing five types of patients with pathological changes under color retinal FIs range from 86.4% to 98.6%. Under conventional retinopathy FIs, the accuracy, recall, and accuracy of the system in diagnosing five types of patients ranged from 70.1% to 85%. The results show that the application of focus color retinal FIs in the intelligent medical system has high accuracy and reliability for the early detection and diagnosis of diabetic retinopathy and has important clinical applications.
视网膜疾病是一种严重且广泛存在的眼科疾病，严重影响患者的视力和生活质量。随着人口老龄化和生活方式的改变，视网膜疾病的发病率逐年上升。然而，传统的诊断方法通常需要经验丰富的医生对眼底图像进行分析和判断，这存在主观性和误诊的风险。本文将分析一种基于局灶性视网膜图像辅助诊断的智能医疗系统，并使用卷积神经网络（CNN）对眼底图像（FI）中的硬性渗出物（HE）进行识别、分类和检测。研究结果表明，在其他条件相同的情况下，该系统在诊断彩色视网膜FI下五种病变类型患者时的准确率、召回率和精确率在86.4%至98.6%之间。在传统视网膜病变FI下，该系统在诊断五种类型患者时的准确率、召回率和精确率在70.1%至85%之间。结果表明，聚焦彩色视网膜FI在智能医疗系统中的应用对糖尿病视网膜病变的早期检测和诊断具有较高的准确性和可靠性，具有重要的临床应用价值。
