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[JAMA最新论文]:有关糖尿病视网膜病变及相关眼病的深度学习系统的建立与验证
2017年12月15日 时讯速递, 进展交流 暂无评论

Original Investigation

December 12, 2017

Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes

Daniel Shu Wei Ting, Carol Yim-Lui Cheung, Gilbert Lim, et al.

JAMA. 2017;318(22):2211-2223. doi:10.1001/jama.2017.18152

Abstract

Importance 背景

A deep learning system (DLS) is a machine learning technology with potential for screening diabetic retinopathy and related eye diseases.

深度学习系统(DLS)是一种机器学习技术,可用于筛查糖尿病视网膜病变及相关眼病。

Objective 目的

To evaluate the performance of a DLS in detecting referable diabetic retinopathy, vision-threatening diabetic retinopathy, possible glaucoma, and age-related macular degeneration (AMD) in community and clinic-based multiethnic populations with diabetes.

在社区及门诊的多种族糖尿病人群,评价DLS发现需就诊的糖尿病视网膜病变,危及视力的糖尿病视网膜病变,可能的青光眼,以及老年性黄斑变性(AMD)的能力。

Design, Setting, and Participants 设计,场景与研究对象

Diagnostic performance of a DLS for diabetic retinopathy and related eye diseases was evaluated using 494 661 retinal images. A DLS was trained for detecting diabetic retinopathy (using 76 370 images), possible glaucoma (125 189 images), and AMD (72 610 images), and performance of DLS was evaluated for detecting diabetic retinopathy (using 112 648 images), possible glaucoma (71 896 images), and AMD (35 948 images). Training of the DLS was completed in May 2016, and validation of the DLS was completed in May 2017 for detection of referable diabetic retinopathy (moderate nonproliferative diabetic retinopathy or worse) and vision-threatening diabetic retinopathy (severe nonproliferative diabetic retinopathy or worse) using a primary validation data set in the Singapore National Diabetic Retinopathy Screening Program and 10 multiethnic cohorts with diabetes.

根据494661幅视网膜影像,评估DLS诊断糖尿病视网膜病变及相关眼病的准确性。通过DLS检测糖尿病视网膜病变(76370幅图像)、可能的青光眼(125189幅图像)及AMD(72610幅图像)的训练,对于DLS诊断糖尿病视网膜病变(112648幅图像)、可能的青光眼(71896幅图像)及AMD(35948幅图像)的准确性进行评估。2016年5月完成DLS的训练,并采用新加坡国立糖尿病视网膜病变筛查系统以及10个多种族糖尿病队列的验证数据集,于2017年5月完成DLS检测需就诊的糖尿病视网膜病变(中度或更严重的非增殖性糖尿病视网膜病变)及危及视力的糖尿病视网膜病变(重度或更严重的非增殖性糖尿病视网膜病变)的验证。

Exposures 暴露因素

Use of a deep learning system.

使用深度学习系统

Main Outcomes and Measures 主要预后指标

Area under the receiver operating characteristic curve (AUC) and sensitivity and specificity of the DLS with professional graders (retinal specialists, general ophthalmologists, trained graders, or optometrists) as the reference standard.

以专业评估者(视网膜病变专家,一般眼科医生,接受培训的评估者或验光师)作为参照,评价DLS的受试者工作特征曲线下面积(AUC)及敏感性和特异性。

Results 结果

In the primary validation dataset (n = 14 880 patients; 71 896 images; mean [SD] age, 60.2 [2.2] years; 54.6% men), the prevalence of referable diabetic retinopathy was 3.0%; vision-threatening diabetic retinopathy, 0.6%; possible glaucoma, 0.1%; and AMD, 2.5%. The AUC of the DLS for referable diabetic retinopathy was 0.936 (95% CI, 0.925-0.943), sensitivity was 90.5% (95% CI, 87.3%-93.0%), and specificity was 91.6% (95% CI, 91.0%-92.2%). For vision-threatening diabetic retinopathy, AUC was 0.958 (95% CI, 0.956-0.961), sensitivity was 100% (95% CI, 94.1%-100.0%), and specificity was 91.1% (95% CI, 90.7%-91.4%). For possible glaucoma, AUC was 0.942 (95% CI, 0.929-0.954), sensitivity was 96.4% (95% CI, 81.7%-99.9%), and specificity was 87.2% (95% CI, 86.8%-87.5%). For AMD, AUC was 0.931 (95% CI, 0.928-0.935), sensitivity was 93.2% (95% CI, 91.1%-99.8%), and specificity was 88.7% (95% CI, 88.3%-89.0%). For referable diabetic retinopathy in the 10 additional datasets, AUC range was 0.889 to 0.983 (n = 40 752 images).

在主要验证数据集(n = 14 880名患者;  71 896幅图像;平均 [SD] 年龄, 60.2 [2.2] 岁;男性 54.6%)中,需就诊的糖尿病视网膜病变罹患率为3.0%;危及视力的糖尿病视网膜病变0.6%;可能的青光眼0.1%;AMD 2.5%。DLS检测需就诊的糖尿病视网膜病变的AUC为0.936 (95% CI, 0.925-0.943),敏感性90.5% (95% CI, 87.3%-93.0%),特异性 91.6% (95% CI, 91.0%-92.2%)。对于危及视力的糖尿病视网膜病变,AUC 为 0.942 (95% CI, 0.929-0.954),敏感性 96.4% (95% CI, 81.7%-99.9%),特异性 87.2% (95% CI, 86.8%-87.5%)。对于AMD,AUC 为 0.931 (95% CI, 0.928-0.935),敏感性为 93.2% (95% CI, 91.1%-99.8%),特异性 88.7% (95% CI, 88.3%-89.0%)。采用另外10个数据集,诊断需就诊的糖尿病视网膜病变 AUC 范围介于 0.889 - 0.983(n = 40 752 幅图像)。

Conclusions and Relevance 结论与意义

In this evaluation of retinal images from multiethnic cohorts of patients with diabetes, the DLS had high sensitivity and specificity for identifying diabetic retinopathy and related eye diseases. Further research is necessary to evaluate the applicability of the DLS in health care settings and the utility of the DLS to improve vision outcomes.

在评估多种族糖尿病患者队列的视网膜图像时,DLS诊断糖尿病视网膜病变及相关眼病的敏感性和特异性很高。需要进一步研究评价DLS在医疗环境下的应用及其对于视力的改善作用。

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