Deep Learning-Enabled Point-of-Care Sensing Using Multiplexed Paper-Based Sensors
| Autor: | Hyou-Arm Joung, Omai B. Garner, Aydogan Ozcan, Dino Di Carlo, Artem Goncharov, Jesse Liang, Zachary S. Ballard, Karina Nugroho |
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| Rok vydání: | 2019 |
| Předmět: |
Analyte
Computer science Coefficient of variation Computer applications to medicine. Medical informatics Real-time computing R858-859.7 Medicine (miscellaneous) Health Informatics 02 engineering and technology lcsh:Computer applications to medicine. Medical informatics 01 natural sciences Multiplexing Article Health Information Management Machine learning Vertical flow Sensitivity (control systems) Point of care Assay systems business.industry Deep learning 010401 analytical chemistry Linearity Diagnostic markers Paper based 021001 nanoscience & nanotechnology Serum samples 0104 chemical sciences Computer Science Applications Cardiovascular diseases Medical test Optical sensors lcsh:R858-859.7 Artificial intelligence 0210 nano-technology business |
| Zdroj: | npj Digital Medicine, Vol 3, Iss 1, Pp 1-8 (2020) NPJ Digital Medicine |
| DOI: | 10.1101/667436 |
| Popis: | We present a deep learning-based framework to design and quantify point-of-care sensors. As its proof-of-concept and use-case, we demonstrated a low-cost and rapid paper-based vertical flow assay (VFA) for high sensitivity C-Reactive Protein (hsCRP) testing, a common medical test used for quantifying the degree of inflammation in patients at risk of cardio-vascular disease (CVD). A machine learning-based sensor design framework was developed for two key tasks: (1) to determine an optimal configuration of immunoreaction spots and conditions, spatially-multiplexed on a paper-based sensing membrane, and (2) to accurately infer the target analyte concentration based on the signals of the optimal VFA configuration. Using a custom-designed mobile-phone based VFA reader, a clinical study was performed with 85 human serum samples to characterize the quantification accuracy around the clinically defined cutoffs for CVD risk stratification. Results from blindly-tested VFAs indicate a competitive coefficient of variation of 11.2% with a linearity of R2 = 0.95; in addition to the success in the high-sensitivity CRP range (i.e., 0-10 mg/L), our results further demonstrate a mitigation of the hook-effect at higher CRP concentrations due to the incorporation of antigen capture spots within the multiplexed sensing membrane of the VFA. This paper-based computational VFA that is powered by deep learning could expand access to CVD health screening, and the presented machine learning-enabled sensing framework can be broadly used to design cost-effective and mobile sensors for various point-of-care diagnostics applications. |
| Databáze: | OpenAIRE |
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