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

• Published in: Analytical and bioanalytical chemistry 2022-05, Vol.414 (13), p.3959-3970
• Main Authors: Ning, Qihong, Zheng, Wei, Xu, Hao, Zhu, Armando, Li, Tangan, Cheng, Yuemeng, Feng, Shaoqing, Wang, Li, Cui, Daxiang, Wang, Kan
• Format: Article
• Language: English
• Subjects: Algorithms; Analytical Chemistry; Artificial neural networks; Biochemistry; C-Reactive Protein; Characterization and Evaluation of Materials; Chemistry; Chemistry and Materials Science; Classification; Colorimetry; Food Science; Image acquisition; Image segmentation; Imprinting; Lab-On-A-Chip Devices; Laboratory Medicine; Learning algorithms; Machine Learning; Microfluidic Analytical Techniques; Microfluidic devices; Microfluidics; Monitoring/Environmental Analysis; Multilayer perceptrons; Multilayers; Neural networks; Research Paper; Risk levels
• ISSN: 1618-2642; 1618-2650
• DOI: 10.1007/s00216-022-04039-x

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. Graphical abstract