PURPOSE To develop and evaluate deep learning models that screen multiple abnormal findings in retinal fundus images. DESIGN Cross-sectional study. PARTICIPANTS For the development and testing of deep learning models, 309 786 readings from 103 262 images were used. Two additional external datasets (the Indian Diabetic Retinopathy Image Dataset and e-ophtha) were used for testing. A third external dataset (Messidor) was used for comparison of the models with human experts. METHODS Macula-centered retinal fundus images from the Seoul National University Bundang Hospital Retina Image Archive, obtained at the health screening center and ophthalmology outpatient clinic at Seoul National University Bundang Hospital, were assessed for 12 major findings (hemorrhage, hard exudate, cotton-wool patch, drusen, membrane, macular hole, myelinated nerve fiber, chorioretinal atrophy or scar, any vascular abnormality, retinal nerve fiber layer defect, glaucomatous disc change, and nonglaucomatous disc change) with their regional information using deep learning algorithms. MAIN OUTCOME MEASURES Area under the receiver operating characteristic curve and sensitivity and specificity of the deep learning algorithms at the highest harmonic mean were evaluated and compared with the performance of retina specialists, and visualization of the lesions was qualitatively analyzed. RESULTS Areas under the receiver operating characteristic curves for all findings were high at 96.2% to 99.9% when tested in the in-house dataset. Lesion heatmaps highlight salient regions effectively in various findings. Areas under the receiver operating characteristic curves for diabetic retinopathy-related findings tested in the Indian Diabetic Retinopathy Image Dataset and e-ophtha dataset were 94.7% to 98.0%. The model demonstrated a performance that rivaled that of human experts, especially in the detection of hemorrhage, hard exudate, membrane, macular hole, myelinated nerve fiber, and glaucomatous disc change. CONCLUSIONS Our deep learning algorithms with region guidance showed reliable performance for detection of multiple findings in macula-centered retinal fundus images. These interpretable, as well as reliable, classification outputs open the possibility for clinical use as an automated screening system for retinal fundus images.

中文翻译：

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深度学习模型的开发和验证，用于筛选视网膜底图像中的多个异常发现。

目的开发和评估深度学习模型，以筛选视网膜底图像中的多个异常发现。设计横断面研究。参与者为了开发和测试深度学习模型，使用了103 262张图像中的309 786个读数。测试使用了另外两个外部数据集（印度糖尿病视网膜病变图像数据集和e-ophtha）。使用第三个外部数据集（Messidor）将模型与人类专家进行比较。方法从首尔国立大学盆唐医院视网膜图像档案库中以黄斑为中心的视网膜眼底图像在首尔国立大学盆唐医院的健康检查中心和眼科门诊获得，评估了12个主要发现（出血，硬渗出物，棉绒斑块，玻璃膜疣，膜，黄斑裂孔，有髓神经纤维，脉络膜视网膜萎缩或疤痕，任何血管异常，视网膜神经纤维层缺损，青光眼视盘改变和非青光眼视盘改变）及其区域信息，均使用深度学习算法进行。主要观察指标评估接收器工作特性曲线下的区域以及深度学习算法在最高谐波均值下的灵敏度和特异性，并将其与视网膜专家的表现进行比较，并定性分析病变的可视化。结果在内部数据集中进行测试时，所有发现的接收器工作特性曲线下的面积都很高，达到96.2％至99.9％。病变热图在各种发现中有效地突出显示了显着区域。在印度糖尿病性视网膜病变图像数据集和e-ophtha数据集中测试的与糖尿病性视网膜病变相关的发现的接收器工作特征曲线下的面积为94.7％至98.0％。该模型表现出与人类专家相媲美的性能，尤其是在出血，硬渗出液，膜，黄斑裂孔，有髓神经纤维和青光眼视盘改变的检测方面。结论我们的具有区域指导的深度学习算法在检测以黄斑为中心的视网膜眼底图像中的多个发现方面显示出可靠的性能。这些可解释的以及可靠的分类输出为临床使用作为视网膜底图像的自动筛选系统提供了可能性。该模型表现出与人类专家相媲美的性能，尤其是在出血，硬渗出液，膜，黄斑裂孔，有髓神经纤维和青光眼视盘改变的检测方面。结论我们的具有区域指导的深度学习算法在检测以黄斑为中心的视网膜眼底图像中的多个发现方面显示出可靠的性能。这些可解释的以及可靠的分类输出为临床使用作为视网膜底图像的自动筛选系统提供了可能性。该模型显示出与人类专家相媲美的性能，尤其是在出血，硬渗出液，膜，黄斑裂孔，有髓神经纤维和青光眼视盘改变的检测方面。结论我们的具有区域指导的深度学习算法在检测以黄斑为中心的视网膜眼底图像中的多个发现方面显示出可靠的性能。这些可解释的以及可靠的分类输出为临床使用作为视网膜底图像的自动筛选系统提供了可能性。结论我们的具有区域指导的深度学习算法在检测以黄斑为中心的视网膜眼底图像中的多个发现方面显示出可靠的性能。这些可解释的以及可靠的分类输出为临床使用作为视网膜底图像的自动筛选系统提供了可能性。结论我们的具有区域指导的深度学习算法在检测以黄斑为中心的视网膜眼底图像中的多个发现方面显示出可靠的性能。这些可解释的以及可靠的分类输出为临床使用作为视网膜底图像的自动筛选系统提供了可能性。