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Paper microfluidics with deep learning for portable intelligent nucleic acid amplification tests.
- Source :
-
Talanta [Talanta] 2023 Jun 01; Vol. 258, pp. 124470. Date of Electronic Publication: 2023 Mar 20. - Publication Year :
- 2023
-
Abstract
- During global outbreaks such as COVID-19, regular nucleic acid amplification tests (NAATs) have posed unprecedented burden on hospital resources. Data of traditional NAATs are manually analyzed post assay. Integration of artificial intelligence (AI) with on-chip assays give rise to novel analytical platforms via data-driven models. Here, we combined paper microfluidics, portable optoelectronic system with deep learning for SARS-CoV-2 detection. The system was quite streamlined with low power dissipation. Pixel by pixel signals reflecting amplification of synthesized SARS-CoV-2 templates (containing ORF1ab, N and E genes) can be real-time processed. Then, the data were synchronously fed to the neural networks for early prediction analysis. Instead of the quantification cycle (C <subscript>q</subscript> ) based analytics, reaction dynamics hidden at the early stage of amplification curve were utilized by neural networks for predicting subsequent data. Qualitative and quantitative analysis of the 40-cycle NAATs can be achieved at the end of 22nd cycle, reducing time cost by 45%. In particular, the attention mechanism based deep learning model trained by microfluidics-generated data can be seamlessly adapted to multiple clinical datasets including readouts of SARS-CoV-2 detection. Accuracy, sensitivity and specificity of the prediction can reach up to 98.1%, 97.6% and 98.6%, respectively. The approach can be compatible with the most advanced sensing technologies and AI algorithms to inspire ample innovations in fields of fundamental research and clinical settings.<br />Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.<br /> (Copyright © 2023 Elsevier B.V. All rights reserved.)
Details
- Language :
- English
- ISSN :
- 1873-3573
- Volume :
- 258
- Database :
- MEDLINE
- Journal :
- Talanta
- Publication Type :
- Academic Journal
- Accession number :
- 36958098
- Full Text :
- https://doi.org/10.1016/j.talanta.2023.124470