Deep learning-assisted ultra-accurate smartphone testing of paper-based colorimetric ELISA assays

Duan, Sixuan, Cai, Tianyu, Zhu, Jia, Yang, Xi, Lim, Eng Gee ORCID: 0000-0003-0199-7386, Huang, Kaizhu, Hoettges, Kai ORCID: 0000-0002-0415-1688, Zhang, Quan, Fu, Hao, Guo, Qiang
et al (show 3 more authors) (2023) Deep learning-assisted ultra-accurate smartphone testing of paper-based colorimetric ELISA assays. Analytica Chimica Acta, 1248. p. 340868.

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Smartphone has long been considered as one excellent platform for disease screening and diagnosis, especially when combined with microfluidic paper-based analytical devices (μPADs) that feature low cost, ease of use, and pump-free operations. In this paper, we report a deep learning-assisted smartphone platform for ultra-accurate testing of paper-based microfluidic colorimetric enzyme-linked immunosorbent assay (c-ELISA). Different from existing smartphone-based μPAD platforms, whose sensing reliability is suffered from uncontrolled ambient lighting conditions, our platform is able to eliminate those random lighting influences for enhanced sensing accuracy. We first constructed a dataset that contains c-ELISA results (n = 2048) of rabbit IgG as the model target on μPADs under eight controlled lighting conditions. Those images are then used to train four different mainstream deep learning algorithms. By training with these images, the deep learning algorithms can well eliminate the influences of lighting conditions. Among them, the GoogLeNet algorithm gives the highest accuracy (>97%) in quantitative rabbit IgG concentration classification/prediction, which also provides 4% higher area under curve (AUC) value than that of the traditional curve fitting results analysis method. In addition, we fully automate the whole sensing process and achieve the "image in, answer out" to maximize the convenience of the smartphone. A simple and user-friendly smartphone application has been developed that controls the whole process. This newly developed platform further enhances the sensing performance of μPADs for use by laypersons in low-resource areas and can be facilely adapted to the real disease protein biomarkers detection by c-ELISA on μPADs.

Item Type: Article
Additional Information: Source info: BIOSBE-D-22-02607
Uncontrolled Keywords: Smartphone point -of -care testing, Microfluidic paper -based analytical devices, Colorimetric enzyme -linked immunosorbent, assay, Deep learning
Divisions: Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User: Symplectic Admin
Date Deposited: 22 Feb 2023 12:58
Last Modified: 24 Apr 2023 14:19
DOI: 10.1016/j.aca.2023.340868
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