The Harker School, San Jose, California.
Byers Eye Institute at Stanford, Stanford University School of Medicine, Palo Alto, California.
Ophthalmology. 2017 Jul;124(7):962-969. doi: 10.1016/j.ophtha.2017.02.008. Epub 2017 Mar 27.
Diabetic retinopathy (DR) is one of the leading causes of preventable blindness globally. Performing retinal screening examinations on all diabetic patients is an unmet need, and there are many undiagnosed and untreated cases of DR. The objective of this study was to develop robust diagnostic technology to automate DR screening. Referral of eyes with DR to an ophthalmologist for further evaluation and treatment would aid in reducing the rate of vision loss, enabling timely and accurate diagnoses.
We developed and evaluated a data-driven deep learning algorithm as a novel diagnostic tool for automated DR detection. The algorithm processed color fundus images and classified them as healthy (no retinopathy) or having DR, identifying relevant cases for medical referral.
A total of 75 137 publicly available fundus images from diabetic patients were used to train and test an artificial intelligence model to differentiate healthy fundi from those with DR. A panel of retinal specialists determined the ground truth for our data set before experimentation. We also tested our model using the public MESSIDOR 2 and E-Ophtha databases for external validation. Information learned in our automated method was visualized readily through an automatically generated abnormality heatmap, highlighting subregions within each input fundus image for further clinical review.
We used area under the receiver operating characteristic curve (AUC) as a metric to measure the precision-recall trade-off of our algorithm, reporting associated sensitivity and specificity metrics on the receiver operating characteristic curve.
Our model achieved a 0.97 AUC with a 94% and 98% sensitivity and specificity, respectively, on 5-fold cross-validation using our local data set. Testing against the independent MESSIDOR 2 and E-Ophtha databases achieved a 0.94 and 0.95 AUC score, respectively.
A fully data-driven artificial intelligence-based grading algorithm can be used to screen fundus photographs obtained from diabetic patients and to identify, with high reliability, which cases should be referred to an ophthalmologist for further evaluation and treatment. The implementation of such an algorithm on a global basis could reduce drastically the rate of vision loss attributed to DR.
糖尿病视网膜病变（DR）是全球可预防失明的主要原因之一。对所有糖尿病患者进行视网膜筛查检查尚未得到满足，还有许多未确诊和未经治疗的 DR 病例。本研究的目的是开发强大的诊断技术以实现 DR 筛查的自动化。将患有 DR 的眼睛转介给眼科医生进行进一步评估和治疗，有助于降低视力丧失率，实现及时准确的诊断。
我们开发并评估了一种数据驱动的深度学习算法，作为一种用于自动 DR 检测的新型诊断工具。该算法处理彩色眼底图像并对其进行分类，将其分为健康（无视网膜病变）或患有 DR，并识别出需要医学转介的相关病例。
使用来自糖尿病患者的 75137 张公共眼底图像来训练和测试人工智能模型，以区分健康眼底和患有 DR 的眼底。一组视网膜专家在实验前确定了我们数据集的真实情况。我们还使用公共 MESSIDOR 2 和 E-Ophtha 数据库测试了我们的模型，以进行外部验证。通过自动生成的异常热图，可以轻松地可视化我们自动方法中学到的信息，突出每个输入眼底图像中的子区域以进行进一步的临床审查。
我们使用接收器工作特征曲线下的面积（AUC）作为衡量我们算法的精度-召回权衡的指标，报告接收器工作特征曲线上的相关敏感性和特异性指标。
我们的模型在使用本地数据集进行 5 折交叉验证时，AUC 为 0.97，灵敏度和特异性分别为 94%和 98%。在对独立的 MESSIDOR 2 和 E-Ophtha 数据库进行测试时，AUC 分别达到 0.94 和 0.95。
一种完全基于数据驱动的人工智能分级算法可用于筛选从糖尿病患者获得的眼底照片，并以高可靠性识别应转介给眼科医生进行进一步评估和治疗的病例。在全球范围内实施这样的算法可以大大降低 DR 导致的视力丧失率。
