Abstract：
BACKGROUND: Early disease detection is emphasized within ophthalmology now more than ever, and as a result, clinicians and innovators turn to deep learning to expedite accurate diagnosis and mitigate treatment delay. Efforts concentrate on the creation of deep learning systems that analyze clinical image data to detect disease-specific features with maximum sensitivity. Moreover, these systems hold promise of early accurate diagnosis and treatment of patients with common progressive diseases. DenseNet, ResNet, and VGG-16 are among a few of the deep learning Convolutional Neural Network (CNN) algorithms that have been introduced and are being investigated for potential application within ophthalmology.
METHODS: In this study, the authors sought to create and evaluate a novel ensembled deep learning CNN model that analyzes a dataset of shuffled retinal color fundus images (RCFIs) from eyes with various ocular disease features (cataract, glaucoma, diabetic retinopathy). Our aim was to determine (1) the relative performance of our finalized model in classifying RCFIs according to disease and (2) the diagnostic potential of the finalized model to serve as a screening test for specific diseases (cataract, glaucoma, diabetic retinopathy) upon presentation of RCFIs with diverse disease manifestations.
RESULTS: We found adding convolutional layers to an existing VGG-16 model, which was named as a proposed model in this article that, resulted in significantly increased performance with 98% accuracy (p<0.05), including good diagnostic potential for binary disease detection in cataract, glaucoma, diabetic retinopathy.
CONCLUSIONS: The proposed model was found to be suitable and accurate for a decision support system in Ophthalmology Clinical Framework.
METHODS: In this study, the authors sought to create and evaluate a novel ensembled deep learning CNN model that analyzes a dataset of shuffled retinal color fundus images (RCFIs) from eyes with various ocular disease features (cataract, glaucoma, diabetic retinopathy). Our aim was to determine (1) the relative performance of our finalized model in classifying RCFIs according to disease and (2) the diagnostic potential of the finalized model to serve as a screening test for specific diseases (cataract, glaucoma, diabetic retinopathy) upon presentation of RCFIs with diverse disease manifestations.
RESULTS: We found adding convolutional layers to an existing VGG-16 model, which was named as a proposed model in this article that, resulted in significantly increased performance with 98% accuracy (p<0.05), including good diagnostic potential for binary disease detection in cataract, glaucoma, diabetic retinopathy.
CONCLUSIONS: The proposed model was found to be suitable and accurate for a decision support system in Ophthalmology Clinical Framework.
摘要：
背景：现在比以往任何时候都更强调眼科的早期疾病检测，结果，临床医生和创新者转向深度学习以加快准确诊断并减轻治疗延迟。努力集中在创建深度学习系统，该系统分析临床图像数据，以最大的灵敏度检测疾病特异性特征。此外，这些系统有望对常见进行性疾病的患者进行早期准确诊断和治疗。DenseNet,ResNet,和VGG-16是一些深度学习卷积神经网络（CNN）算法之一，这些算法已经被引入并正在研究在眼科中的潜在应用。
方法：在本研究中，作者试图创建和评估一种新颖的集成深度学习CNN模型，该模型分析了来自具有各种眼部疾病特征（白内障，青光眼,糖尿病视网膜病变)。我们的目标是确定（1）我们最终模型在根据疾病对RCFIs进行分类方面的相对性能，以及（2）最终模型作为特定疾病（白内障，青光眼,糖尿病性视网膜病变）在出现具有多种疾病表现的RCFIs时。
结果：我们发现将卷积层添加到现有的VGG-16模型中，在本文中被命名为一个拟议的模型，显著提高了性能，准确率为98%(p<0.05)，包括在白内障中检测二元疾病的良好诊断潜力，青光眼,糖尿病视网膜病变。
结论：发现所提出的模型适用于眼科临床框架中的决策支持系统。
方法：在本研究中，作者试图创建和评估一种新颖的集成深度学习CNN模型，该模型分析了来自具有各种眼部疾病特征（白内障，青光眼,糖尿病视网膜病变)。我们的目标是确定（1）我们最终模型在根据疾病对RCFIs进行分类方面的相对性能，以及（2）最终模型作为特定疾病（白内障，青光眼,糖尿病性视网膜病变）在出现具有多种疾病表现的RCFIs时。
结果：我们发现将卷积层添加到现有的VGG-16模型中，在本文中被命名为一个拟议的模型，显著提高了性能，准确率为98%(p<0.05)，包括在白内障中检测二元疾病的良好诊断潜力，青光眼,糖尿病视网膜病变。
结论：发现所提出的模型适用于眼科临床框架中的决策支持系统。
