Zhao Xinyu, Meng Lihui, Su Hao, Lv Bin, Lv Chuanfeng, Xie Guotong, Chen Youxin
Department of Ophthalmology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China.
Key Lab of Ocular Fundus Diseases, Chinese Academy of Medical Sciences, Beijing, China.
Front Cell Dev Biol. 2022 May 19;10:888268. doi: 10.3389/fcell.2022.888268. eCollection 2022.
Anemia is the most common hematological disorder. The purpose of this study was to establish and validate a deep-learning model to predict Hgb concentrations and screen anemia using ultra-wide-field (UWF) fundus images. The study was conducted at Peking Union Medical College Hospital. Optos color images taken between January 2017 and June 2021 were screened for building the dataset. ASModel_UWF using UWF images was developed. Mean absolute error (MAE) and area under the receiver operating characteristics curve (AUC) were used to evaluate its performance. Saliency maps were generated to make the visual explanation of the model. ASModel_UWF acquired the MAE of the prediction task of 0.83 g/dl (95%CI: 0.81-0.85 g/dl) and the AUC of the screening task of 0.93 (95%CI: 0.92-0.95). Compared with other screening approaches, it achieved the best performance of AUC and sensitivity when the test dataset size was larger than 1000. The model tended to focus on the area around the optic disc, retinal vessels, and some regions located at the peripheral area of the retina, which were undetected by non-UWF imaging. The deep-learning model ASModel_UWF could both predict Hgb concentration and screen anemia in a non-invasive and accurate way with high efficiency.
贫血是最常见的血液系统疾病。本研究的目的是建立并验证一种深度学习模型，以使用超广角（UWF）眼底图像预测血红蛋白（Hgb）浓度并筛查贫血。该研究在北京协和医院进行。筛选了2017年1月至2021年6月期间拍摄的Optos彩色图像以构建数据集。开发了使用UWF图像的ASModel_UWF。使用平均绝对误差（MAE）和受试者工作特征曲线下面积（AUC）来评估其性能。生成显著性图以对模型进行可视化解释。ASModel_UWF在预测任务中获得的MAE为0.83 g/dl（95%CI：0.81 - 0.85 g/dl），在筛查任务中获得的AUC为0.93（95%CI：0.92 - 0.95）。与其他筛查方法相比，当测试数据集大小大于1000时，它在AUC和灵敏度方面取得了最佳性能。该模型倾向于关注视盘、视网膜血管周围区域以及视网膜周边区域的一些部位，这些部位是普通眼底成像无法检测到的。深度学习模型ASModel_UWF能够以非侵入性、准确且高效的方式预测Hgb浓度并筛查贫血。
