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Rapid segmentation and sensitive analysis of CRP with paper-based microfluidic device using machine learning

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Abstract

Microfluidic paper-based analytical devices (μPADs) have been widely used in point-of-care testing owing to their simple operation, low volume of the sample required, and the lack of the need for an external force. To obtain accurate semi-quantitative or quantitative results, μPADs need to respond to the challenges posed by differences in reaction conditions. In this paper, multi-layer μPADs are fabricated by the imprinting method for the colorimetric detection of C-reactive protein (CRP). Different lighting conditions and shooting angles of scenes are simulated in image acquisition, and the detection-related performance of μPADs is improved by using a machine learning algorithm. The You Only Look Once (YOLO) model is used to identify the areas of reaction in μPADs. This model can observe an image only once to predict the objects present in it and their locations. The YOLO model trained in this study was able to identify all the reaction areas quickly without incurring any error. These reaction areas were categorized by classification algorithms to determine the risk level of CRP concentration. Multi-layer perceptron, convolutional neural network, and residual network algorithms were used for the classification tasks, where the latter yielded the highest accuracy of 96%. It has a promising application prospect in fast recognition and analysis of μPADs.

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Author notes

  1. Qihong Ning and Wei Zheng contributed equally to this work.

Authors and Affiliations

  1. School of Sensing Science and Engineering, School of Electronic Information and Electrical Engineering, Key Laboratory of Thin Film and Microfabrication Technology (Ministry of Education), Shanghai Jiao Tong University, Shanghai, 200240, China

    Qihong Ning, Wei Zheng, Armando Zhu, Tangan Li, Yuemeng Cheng, Daxiang Cui & Kan Wang

  2. School of Naval Architecture, Ocean & Civil Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China

    Hao Xu

  3. Department of Plastic and Reconstructive Surgery, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China

    Shaoqing Feng

  4. School of Electrical Engineering, Henan University of Technology, Zhengzhou, 450007, Henan, China

    Li Wang

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  1. Qihong Ning
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Ning, Q., Zheng, W., Xu, H. et al. Rapid segmentation and sensitive analysis of CRP with paper-based microfluidic device using machine learning. Anal Bioanal Chem 414, 3959–3970 (2022). https://doi.org/10.1007/s00216-022-04039-x

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