Zhu Shaojun, Lu Bing, Wang Chenghu, Wu Maonian, Zheng Bo, Jiang Qin, Wei Ruili, Cao Qixin, Yang Weihua
School of Information Engineering, Huzhou University, Huzhou, China.
Zhejiang Province Key Laboratory of Smart Management and Application of Modern Agricultural Resources, Huzhou University, Huzhou, China.
Front Med (Lausanne). 2022 Feb 23;9:808402. doi: 10.3389/fmed.2022.808402. eCollection 2022.
A six-category model of common retinal diseases is proposed to help primary medical institutions in the preliminary screening of the five common retinal diseases.
A total of 2,400 fundus images of normal and five common retinal diseases were provided by a cooperative hospital. Two six-category deep learning models of common retinal diseases based on the EfficientNet-B4 and ResNet50 models were trained. The results from the six-category models in this study and the results from a five-category model in our previous study based on ResNet50 were compared. A total of 1,315 fundus images were used to test the models, the clinical diagnosis results and the diagnosis results of the two six-category models were compared. The main evaluation indicators were sensitivity, specificity, F1-score, area under the curve (AUC), 95% confidence interval, kappa and accuracy, and the receiver operator characteristic curves of the two six-category models were compared in the study.
The diagnostic accuracy rate of EfficientNet-B4 model was 95.59%, the kappa value was 94.61%, and there was high diagnostic consistency. The AUC of the normal diagnosis and the five retinal diseases were all above 0.95. The sensitivity, specificity, and F1-score for the diagnosis of normal fundus images were 100, 99.9, and 99.83%, respectively. The specificity and F1-score for RVO diagnosis were 95.68, 98.61, and 93.09%, respectively. The sensitivity, specificity, and F1-score for high myopia diagnosis were 96.1, 99.6, and 97.37%, respectively. The sensitivity, specificity, and F1-score for glaucoma diagnosis were 97.62, 99.07, and 94.62%, respectively. The sensitivity, specificity, and F1-score for DR diagnosis were 90.76, 99.16, and 93.3%, respectively. The sensitivity, specificity, and F1-score for MD diagnosis were 92.27, 98.5, and 91.51%, respectively.
The EfficientNet-B4 model was used to design a six-category model of common retinal diseases. It can be used to diagnose the normal fundus and five common retinal diseases based on fundus images. It can help primary doctors in the screening for common retinal diseases, and give suitable suggestions and recommendations. Timely referral can improve the efficiency of diagnosis of eye diseases in rural areas and avoid delaying treatment.
提出一种常见视网膜疾病的六分类模型，以帮助基层医疗机构对五种常见视网膜疾病进行初步筛查。
合作医院提供了总共2400张正常及五种常见视网膜疾病的眼底图像。基于EfficientNet - B4和ResNet50模型训练了两种常见视网膜疾病的六分类深度学习模型。比较了本研究中六分类模型的结果与我们之前基于ResNet50的五分类模型的结果。总共1315张眼底图像用于测试模型，比较了临床诊断结果与两种六分类模型的诊断结果。主要评估指标为灵敏度、特异度、F1分数、曲线下面积（AUC）、95%置信区间、kappa值和准确率，并在研究中比较了两种六分类模型的受试者工作特征曲线。
EfficientNet - B4模型的诊断准确率为95.59%，kappa值为94.61%，具有较高的诊断一致性。正常诊断及五种视网膜疾病的AUC均高于0.95。诊断正常眼底图像的灵敏度分别为100%、特异度为99.9%、F1分数为99.83%。诊断视网膜静脉阻塞（RVO）的特异度、F1分数分别为95.68%、98.61%、93.09%。诊断高度近视的灵敏度、特异度、F1分数分别为96.1%、99.6%、97.37%。诊断青光眼的灵敏度、特异度、F1分数分别为97.62%、99.07%、94.62%。诊断糖尿病性视网膜病变（DR）的灵敏度、特异度、F1分数分别为90.76%、99.16%、93.3%。诊断黄斑病变（MD）的灵敏度、特异度、F1分数分别为92.27%、98.5%、91.51%。
使用EfficientNet - B4模型设计了常见视网膜疾病的六分类模型。它可用于基于眼底图像诊断正常眼底及五种常见视网膜疾病。它可以帮助基层医生筛查常见视网膜疾病，并给出合适的建议和推荐。及时转诊可提高农村地区眼病诊断效率，避免延误治疗。
