Predicting myocardial infarction through retinal scans and minimal personal information
Predicting myocardial infarction through retinal scans and minimal personal information
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DOI：
10.1038/s42256-021-00427-7
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发表时间：
2022-01-25
影响因子：
23.8
通讯作者：
Frangi, Alejandro F.
中科院分区：
文献类型：
--
作者：
Diaz-Pinto, Andres;Ravikumar, Nishant;Frangi, Alejandro F.
在眼科实践中，通常会获得视网膜图像，以诊断和监测原发眼部疾病和影响眼睛的全身疾病，如糖尿病视网膜病变。最近的研究表明，视网膜图像上的生物标记物，例如视网膜血管密度或曲线度，与心功能有关，并可能识别有冠状动脉疾病风险的患者。在这项工作中，我们研究了视网膜图像和相关患者元数据的使用，以估计左室质量和左室舒张末期容量，并随后预测发生的心肌梗死。我们训练了一个多通道变量自动编码器和一个深度回归模型来估计左室质量(4.4(-32.30，41.1)g)和左室舒张末容量(3.02(-53.45，59.49)ml)，并预测心肌梗死的风险(AUC=0.80+/-0.02，灵敏度=0.74+/-0.02，特异度=0.71+/-0.03)。我们的结果表明，人们可以从每个眼镜店和眼科诊所提供的视网膜成像中识别出未来心肌梗死的高风险患者。常规眼科临床成像可以帮助筛查有心血管风险的患者，因为研究表明视网膜和心脏中的生物标记物之间存在强烈的相关性。这一潜力得到了一项采用深度学习模型的多模式研究的支持，该模型可以根据视网膜图像和人口统计数据推断心脏功能指数。
In ophthalmologic practice, retinal images are routinely obtained to diagnose and monitor primary eye diseases and systemic conditions affecting the eye, such as diabetic retinopathy. Recent studies have shown that biomarkers on retinal images, for example, retinal blood vessel density or tortuosity, are associated with cardiac function and may identify patients at risk of coronary artery disease. In this work we investigate the use of retinal images, alongside relevant patient metadata, to estimate left ventricular mass and left ventricular end-diastolic volume, and subsequently, predict incident myocardial infarction. We trained a multichannel variational autoencoder and a deep regressor model to estimate left ventricular mass (4.4 (-32.30, 41.1) g) and left ventricular end-diastolic volume (3.02 (-53.45, 59.49) ml) and predict risk of myocardial infarction (AUC = 0.80 +/- 0.02, sensitivity = 0.74 +/- 0.02, specificity = 0.71 +/- 0.03) using just the retinal images and demographic data. Our results indicate that one could identify patients at high risk of future myocardial infarction from retinal imaging available in every optician and eye clinic.Routine eye clinic imaging could help screen patients with cardiovascular risk as studies indicate strong associations between biomarkers in the retina and the heart. This potential is supported by a multimodal study, employing a deep learning model, that can infer cardiac functional indices based on retinal images and demographic data.