Owing to the invasiveness of diagnostic tests for anaemia and the costs associated with screening for it, the condition is often undetected. Here, we show that anaemia can be detected via machine-learning algorithms trained using retinal fundus images, study participant metadata (including race or ethnicity, age, sex and blood pressure) or the combination of both data types (images and study participant metadata). In a validation dataset of 11,388 study participants from the UK Biobank, the fundus-image-only, metadata-only and combined models predicted haemoglobin concentration (in g dl-1) with mean absolute error values of 0.73 (95% confidence interval: 0.72-0.74), 0.67 (0.66-0.68) and 0.63 (0.62-0.64), respectively, and with areas under the receiver operating characteristic curve (AUC) values of 0.74 (0.71-0.76), 0.87 (0.85-0.89) and 0.88 (0.86-0.89), respectively. For 539 study participants with self-reported diabetes, the combined model predicted haemoglobin concentration with a mean absolute error of 0.73 (0.68-0.78) and anaemia an AUC of 0.89 (0.85-0.93). Automated anaemia screening on the basis of fundus images could particularly aid patients with diabetes undergoing regular retinal imaging and for whom anaemia can increase morbidity and mortality risks.

中文翻译：

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通过深度学习从视网膜眼底图像中检测贫血。

由于贫血诊断测试的侵入性以及与贫血筛查相关的费用，这种情况常常未被发现。在这里，我们表明，可以通过使用视网膜眼底图像、研究参与者元数据（包括种族或民族、年龄、性别和血压）或两种数据类型的组合（图像和研究参与者元数据）训练的机器学习算法来检测贫血。 。在来自英国生物银行的 11,388 名研究参与者的验证数据集中，仅眼底图像、仅元数据和组合模型预测的血红蛋白浓度（以 g dl-1 为单位）的平均绝对误差值为 0.73（95% 置信区间：0.72）分别为 -0.74)、0.67 (0.66-0.68) 和 0.63 (0.62-0.64)，受试者工作特征曲线下面积 (AUC) 值为 0.74 (0.71-0.76)、0.87 (0.85-0.89) 和 0.88 ( 0.86-0.89），分别。对于 539 名自我报告患有糖尿病的研究参与者，组合模型预测血红蛋白浓度的平均绝对误差为 0.73 (0.68-0.78)，贫血的 AUC 为 0.89 (0.85-0.93)。基于眼底图像的自动贫血筛查尤其可以帮助定期进行视网膜成像的糖尿病患者，对于这些患者来说，贫血会增加发病和死亡风险。