Retina Division, Wilmer Eye Institute, The Johns Hopkins University School of Medicine and Hospital, Baltimore, Maryland.
Retina Division, Duke Eye Center, Durham, North Carolina.
JAMA Ophthalmol. 2021 Feb 1;139(2):206-213. doi: 10.1001/jamaophthalmol.2020.5900.
Adherence to screening for vision-threatening proliferative sickle cell retinopathy is limited among patients with sickle cell hemoglobinopathy despite guidelines recommending dilated fundus examinations beginning in childhood. An automated algorithm for detecting sea fan neovascularization from ultra-widefield color fundus photographs could expand access to rapid retinal evaluations to identify patients at risk of vision loss from proliferative sickle cell retinopathy.
To develop a deep learning system for detecting sea fan neovascularization from ultra-widefield color fundus photographs from patients with sickle cell hemoglobinopathy.
DESIGN, SETTING, AND PARTICIPANTS: In a cross-sectional study conducted at a single-institution, tertiary academic referral center, deidentified, retrospectively collected, ultra-widefield color fundus photographs from 190 adults with sickle cell hemoglobinopathy were independently graded by 2 masked retinal specialists for presence or absence of sea fan neovascularization. A third masked retinal specialist regraded images with discordant or indeterminate grades. Consensus retinal specialist reference standard grades were used to train a convolutional neural network to classify images for presence or absence of sea fan neovascularization. Participants included nondiabetic adults with sickle cell hemoglobinopathy receiving care from a Wilmer Eye Institute retinal specialist; the patients had received no previous laser or surgical treatment for sickle cell retinopathy and underwent imaging with ultra-widefield color fundus photographs between January 1, 2012, and January 30, 2019.
Deidentified ultra-widefield color fundus photographs were retrospectively collected.
Sensitivity, specificity, and area under the receiver operating characteristic curve of the convolutional neural network for sea fan detection.
A total of 1182 images from 190 patients were included. Of the 190 patients, 101 were women (53.2%), and the mean (SD) age at baseline was 36.2 (12.3) years; 119 patients (62.6%) had hemoglobin SS disease and 46 (24.2%) had hemoglobin SC disease. One hundred seventy-nine patients (94.2%) were of Black or African descent. Images with sea fan neovascularization were obtained in 57 patients (30.0%). The convolutional neural network had an area under the curve of 0.988 (95% CI, 0.969-0.999), with sensitivity of 97.4% (95% CI, 86.5%-99.9%) and specificity of 97.0% (95% CI, 93.5%-98.9%) for detecting sea fan neovascularization from ultra-widefield color fundus photographs.
This study reports an automated system with high sensitivity and specificity for detecting sea fan neovascularization from ultra-widefield color fundus photographs from patients with sickle cell hemoglobinopathy, with potential applications for improving screening for vision-threatening proliferative sickle cell retinopathy.
尽管指南建议从儿童期开始进行散瞳眼底检查以筛查威胁视力的镰状细胞增殖性视网膜病变，但患有镰状细胞血红蛋白病的患者对该疾病的筛查依从性有限。一种用于从超广角彩色眼底照片中检测海扇新生血管的深度学习算法可以扩大快速视网膜评估的机会，以识别患有增殖性镰状细胞性视网膜病变导致视力丧失风险的患者。
开发一种从镰状细胞血红蛋白病患者的超广角彩色眼底照片中检测海扇新生血管的深度学习系统。
设计、地点和参与者：在一项单中心、三级学术转诊中心进行的横断面研究中，从 190 名镰状细胞血红蛋白病成年患者中独立收集了 2 名盲法视网膜专家对超广角彩色眼底照片的海扇新生血管存在或不存在进行分级。第三名盲法视网膜专家对图像进行了重新分级，以解决不一致或不确定的分级。使用盲法视网膜专家的共识参考标准分级来训练卷积神经网络，以分类图像中海扇新生血管的存在或不存在。参与者包括从威尔默眼科研究所的视网膜专家处接受镰状细胞血红蛋白病治疗的非糖尿病成年患者；这些患者之前未接受过镰状细胞性视网膜病变的激光或手术治疗，并且在 2012 年 1 月 1 日至 2019 年 1 月 30 日期间进行了超广角彩色眼底照片成像。
回顾性收集超广角彩色眼底照片。
卷积神经网络对海扇检测的敏感性、特异性和受试者工作特征曲线下面积。
共纳入 190 例患者的 1182 张图像。190 名患者中，女性 101 名（53.2%），基线时的平均（SD）年龄为 36.2（12.3）岁；119 名患者（62.6%）患有血红蛋白 SS 病，46 名患者（24.2%）患有血红蛋白 SC 病。179 名患者（94.2%）为黑人和非洲裔。57 名患者（30.0%）获得了海扇新生血管图像。卷积神经网络的曲线下面积为 0.988（95%CI，0.969-0.999），对超广角彩色眼底照片中海扇新生血管的敏感性为 97.4%（95%CI，86.5%-99.9%），特异性为 97.0%（95%CI，93.5%-98.9%）。
本研究报告了一种从镰状细胞血红蛋白病患者的超广角彩色眼底照片中检测海扇新生血管的自动化系统，该系统具有较高的敏感性和特异性，可用于改善威胁视力的增殖性镰状细胞性视网膜病变的筛查。
