Deep learning-enabled point-of-care sensing using multiplexed paper-based sensors
Deep learning-enabled point-of-care sensing using multiplexed paper-based sensors
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DOI：
10.1038/s41746-020-0274-y
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发表时间：
2020-05-07
影响因子：
15.2
通讯作者：
Ozcan, Aydogan
中科院分区：
文献类型：
--
作者：
Ballard, Zachary S.;Joung, Hyou-Arm;Ozcan, Aydogan
我们提出了一个基于深度学习的框架来设计和量化护理点传感器。作为一个用例，我们展示了一种低成本、快速的基于纸张的垂直流动试验（VFA），用于高灵敏度的c反应蛋白（hsCRP）检测，通常用于评估心血管疾病（CVD）的风险。开发了一个基于机器学习的框架，以(1)确定免疫反应点和条件的最佳配置，在传感膜上进行空间复用；(2)准确推断目标分析物浓度。使用定制的手持式VFA读取器，85个人体样本的临床研究显示，在hsCRP范围内（即0-10 mg/L），盲测VFA的竞争变异系数为11.2%，线性R-2 = 0.95。我们还证明了由于感应膜上的多重免疫反应而减轻了钩效应。这种基于纸张的计算VFA可以扩展CVD测试的访问，并且所提出的框架可以广泛用于设计具有成本效益的移动医疗点传感器。
We present a deep learning-based framework to design and quantify point-of-care sensors. As a use-case, we demonstrated a low-cost and rapid paper-based vertical flow assay (VFA) for high sensitivity C-Reactive Protein (hsCRP) testing, commonly used for assessing risk of cardio-vascular disease (CVD). A machine learning-based framework was developed to (1) determine an optimal configuration of immunoreaction spots and conditions, spatially-multiplexed on a sensing membrane, and (2) to accurately infer target analyte concentration. Using a custom-designed handheld VFA reader, a clinical study with 85 human samples showed a competitive coefficient-of-variation of 11.2% and linearity of R-2 = 0.95 among blindly-tested VFAs in the hsCRP range (i.e., 0-10 mg/L). We also demonstrated a mitigation of the hook-effect due to the multiplexed immunoreactions on the sensing membrane. This paper-based computational VFA could expand access to CVD testing, and the presented framework can be broadly used to design cost-effective and mobile point-of-care sensors.