Bryan John M, Bryar Paul J, Mirza Rukhsana G
Department of Ophthalmology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.
Telemed J E Health. 2023 Sep;29(9):1349-1355. doi: 10.1089/tmj.2022.0357. Epub 2023 Feb 2.
Purpose:Diabetic retinopathy (DR) is a microvascular complication of diabetes mellitus (DM). Standard of care for patients with DM is an annual eye examination or retinal imaging to assess for DR, the latter of which may be completed through telemedicine approaches. One significant issue is poor-quality images that prevent adequate screening and are thus ungradable. We used artificial intelligence to enable point-of-care (at time of imaging) identification of ungradable images in a DR screening program.
Methods:Nonmydriatic retinal images were gathered from patients with DM imaged during a primary care or endocrinology visit from September 1, 2017, to June 1, 2021. The Topcon TRC-NW400 retinal camera (Topcon Corp., Tokyo, Japan) was used. Images were interpreted by 5 ophthalmologists for gradeability, presence and stage of DR, and presence of non-DR pathologies. A convolutional neural network with Inception V3 network architecture was trained to assess image gradeability. Images were divided into training and test sets, and 10-fold cross-validation was performed.
Results:A total of 1,377 images from 537 patients (56.1% female, median age 58) were analyzed. Ophthalmologists classified 25.9% of images as ungradable. Of gradable images, 18.6% had DR of varying degrees and 26.5% had non-DR pathology. 10 fold cross-validation produced an average area under receiver operating characteristic curve (AUC) of 0.922 (standard deviation: 0.027, range: 0.882 to 0.961). The final model exhibited similar test set performance with an AUC of 0.924.
Conclusions:This model accurately assesses gradeability of nonmydriatic retinal images. It could be used for increasing the efficiency of DR screening programs by enabling point-of-care identification of poor-quality images.
糖尿病视网膜病变（DR）是糖尿病（DM）的一种微血管并发症。糖尿病患者的标准护理是每年进行一次眼部检查或视网膜成像以评估是否患有DR，后者可通过远程医疗方法完成。一个重要问题是图像质量差，这会妨碍充分筛查，因此无法分级。我们使用人工智能在DR筛查项目中实现即时（成像时）识别不可分级图像。
从2017年9月1日至2021年6月1日在初级保健或内分泌科就诊期间接受成像的糖尿病患者中收集非散瞳视网膜图像。使用拓普康TRC-NW400视网膜相机（日本东京拓普康公司）。由5名眼科医生对图像进行可分级性、DR的存在和阶段以及非DR病变的存在情况的解读。训练了一个具有Inception V3网络架构的卷积神经网络来评估图像的可分级性。将图像分为训练集和测试集，并进行10折交叉验证。
共分析了来自537名患者（56.1%为女性，中位年龄58岁）的1377张图像。眼科医生将25.9%的图像分类为不可分级。在可分级图像中，18.6%有不同程度的DR，26.5%有非DR病变。10折交叉验证产生的受试者操作特征曲线（AUC）下的平均面积为0.922（标准差：0.027，范围：0.882至0.961）。最终模型在测试集上表现出相似的性能，AUC为0.924。
该模型准确评估非散瞳视网膜图像的可分级性。它可用于通过在即时护理时识别质量差的图像来提高DR筛查项目的效率。
