Abstract：
BACKGROUND: Rhegmatogenous retinal detachment (RRD) is one of the most common fundus diseases. Many rural areas of China have few ophthalmologists, and ophthalmologic ultrasound examination is of great significance for remote diagnosis of RRD. Therefore, this study aimed to develop and evaluate a deep learning (DL) model, to be used for automated RRD diagnosis based on ophthalmologic ultrasound images, in order to support timely diagnosis of RRD in rural and remote areas.
METHODS: A total of 6,000 ophthalmologic ultrasound images from 1,645 participants were used to train and verify the DL model. A total of 5,000 images were used for training and validating DL models, and an independent testing set of 1,000 images was used to test the performance of eight DL models trained using four different DL model architectures (fully connected neural network, LeNet5, AlexNet, and VGG16) and two preprocessing techniques (original, original image augmented). Receiver operating characteristic (ROC) curves were used to analyze their performance. Heatmaps were generated to visualize the process of the best DL model in the identification of RRD. Finally, five ophthalmologists were invited to diagnose RRD independently on the same test set of 1,000 images for performance comparison with the best DL model.
RESULTS: The best DL model for identifying RRD achieved an area under the ROC curve (AUC) of 0.998 with a sensitivity and specificity of 99.2% and 99.8%, respectively. The best preprocessing method in each model architecture was the application of original image augmentation (average AUC = 0.982). The best model architecture in each preprocessing method was VGG16 (average AUC = 0.998).
CONCLUSIONS: The best DL model determined in this study has higher accuracy, sensitivity, and specificity than the ophthalmologists\' diagnosis in identifying RRD based on ophthalmologic ultrasound images. This model may provide support for timely diagnosis in locations without access to ophthalmologic care.
METHODS: A total of 6,000 ophthalmologic ultrasound images from 1,645 participants were used to train and verify the DL model. A total of 5,000 images were used for training and validating DL models, and an independent testing set of 1,000 images was used to test the performance of eight DL models trained using four different DL model architectures (fully connected neural network, LeNet5, AlexNet, and VGG16) and two preprocessing techniques (original, original image augmented). Receiver operating characteristic (ROC) curves were used to analyze their performance. Heatmaps were generated to visualize the process of the best DL model in the identification of RRD. Finally, five ophthalmologists were invited to diagnose RRD independently on the same test set of 1,000 images for performance comparison with the best DL model.
RESULTS: The best DL model for identifying RRD achieved an area under the ROC curve (AUC) of 0.998 with a sensitivity and specificity of 99.2% and 99.8%, respectively. The best preprocessing method in each model architecture was the application of original image augmentation (average AUC = 0.982). The best model architecture in each preprocessing method was VGG16 (average AUC = 0.998).
CONCLUSIONS: The best DL model determined in this study has higher accuracy, sensitivity, and specificity than the ophthalmologists\' diagnosis in identifying RRD based on ophthalmologic ultrasound images. This model may provide support for timely diagnosis in locations without access to ophthalmologic care.
摘要：
背景：孔源性视网膜脱离（RRD）是最常见的眼底疾病之一。中国许多农村地区几乎没有眼科医生，眼科超声检查对RRD的远程诊断具有重要意义。因此,本研究旨在开发和评估深度学习(DL)模型，用于基于眼科超声图像的自动RRD诊断，以支持农村和偏远地区RRD的及时诊断。
方法：总共使用来自1,645名参与者的6,000张眼科超声图像来训练和验证DL模型。5000张图像用于训练和验证DL模型，并使用1000张图像的独立测试集来测试使用四种不同的DL模型架构（全连接神经网络（FCNN），Lenet5,AlexNet,和VGG16)和两种预处理技术(原始，原始图像增强)。使用接收器工作特征（ROC）曲线来分析其性能。生成热图以可视化RRD识别中的最佳DL模型的过程。最后,5名眼科医生被邀请在同一测试集的1000张图像上独立诊断RRD,以便与最佳DL模型进行性能比较.
结果：鉴定RRD的最佳DL模型实现了0.998的ROC曲线下面积（AUC），灵敏度和特异性分别为99.2％和99.8％，分别。每个模型体系结构中的最佳预处理方法是原始图像增强的应用（平均AUC=0.982）。每种预处理方法中的最佳模型架构为VGG16(平均AUC=0.998)。
结论：本研究确定的最佳DL模型具有更高的准确性，在根据眼科超声图像识别RRD方面，其敏感性和特异性优于眼科医生的诊断。该模型可以为在没有获得眼科护理的情况下的位置的及时诊断提供支持。
方法：总共使用来自1,645名参与者的6,000张眼科超声图像来训练和验证DL模型。5000张图像用于训练和验证DL模型，并使用1000张图像的独立测试集来测试使用四种不同的DL模型架构（全连接神经网络（FCNN），Lenet5,AlexNet,和VGG16)和两种预处理技术(原始，原始图像增强)。使用接收器工作特征（ROC）曲线来分析其性能。生成热图以可视化RRD识别中的最佳DL模型的过程。最后,5名眼科医生被邀请在同一测试集的1000张图像上独立诊断RRD,以便与最佳DL模型进行性能比较.
结果：鉴定RRD的最佳DL模型实现了0.998的ROC曲线下面积（AUC），灵敏度和特异性分别为99.2％和99.8％，分别。每个模型体系结构中的最佳预处理方法是原始图像增强的应用（平均AUC=0.982）。每种预处理方法中的最佳模型架构为VGG16(平均AUC=0.998)。
结论：本研究确定的最佳DL模型具有更高的准确性，在根据眼科超声图像识别RRD方面，其敏感性和特异性优于眼科医生的诊断。该模型可以为在没有获得眼科护理的情况下的位置的及时诊断提供支持。
