Duan, Sixuan, Cai, Tianyu, Liu, Fuyuan, Li, Yifan, Yuan, Hang, Yuan, Wenwen, Huang, Kaizhu, Hoettges, Kai 
ORCID: 0000-0002-0415-1688, Chen, Min, Lim, Eng Gee ORCID: 0000-0003-0199-7386 et al (show 2 more authors)
(2024)
Automatic offline-capable smartphone paper-based microfluidic device for efficient Alzheimer's disease detection.
Analytica Chimica Acta, 1308.
p. 342575.
|
Text
2024 paper based alzheimer.pdf - Author Accepted Manuscript Available under License Creative Commons Attribution. Download (797kB) | Preview |
Abstract
<h4>Background</h4>Alzheimer's disease (AD) is a prevalent neurodegenerative disease with no effective treatment. Efficient and rapid detection plays a crucial role in mitigating and managing AD progression. Deep learning-assisted smartphone-based microfluidic paper analysis devices (μPADs) offer the advantages of low cost, good sensitivity, and rapid detection, providing a strategic pathway to address large-scale disease screening in resource-limited areas. However, existing smartphone-based detection platforms usually rely on large devices or cloud servers for data transfer and processing. Additionally, the implementation of automated colorimetric enzyme-linked immunoassay (c-ELISA) on μPADs can further facilitate the realization of smartphone μPADs platforms for efficient disease detection.<h4>Results</h4>This paper introduces a new deep learning-assisted offline smartphone platform for early AD screening, offering rapid disease detection in low-resource areas. The proposed platform features a simple mechanical rotating structure controlled by a smartphone, enabling fully automated c-ELISA on μPADs. Our platform successfully applied sandwich c-ELISA for detecting the β-amyloid peptide 1-42 (Aβ 1-42, a crucial AD biomarker) and demonstrated its efficacy in 38 artificial plasma samples (healthy: 19, unhealthy: 19, N = 6). Moreover, we employed the YOLOv5 deep learning model and achieved an impressive 97 % accuracy on a dataset of 1824 images, which is 10.16 % higher than the traditional method of curve-fitting results. The trained YOLOv5 model was seamlessly integrated into the smartphone using the NCNN (Tencent's Neural Network Inference Framework), enabling deep learning-assisted offline detection. A user-friendly smartphone application was developed to control the entire process, realizing a streamlined "samples in, answers out" approach.<h4>Significance</h4>This deep learning-assisted, low-cost, user-friendly, highly stable, and rapid-response automated offline smartphone-based detection platform represents a good advancement in point-of-care testing (POCT). Moreover, our platform provides a feasible approach for efficient AD detection by examining the level of Aβ 1-42, particularly in areas with low resources and limited communication infrastructure.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Humans, Alzheimer Disease, Peptide Fragments, Enzyme-Linked Immunosorbent Assay, Microfluidic Analytical Techniques, Paper, Automation, Lab-On-A-Chip Devices, Amyloid beta-Peptides, Biomarkers, Smartphone, Deep Learning |
| Divisions: | Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 15 Apr 2024 15:25 |
| Last Modified: | 16 May 2024 10:10 |
| DOI: | 10.1016/j.aca.2024.342575 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3180349 |
Dimensions
Dimensions
