Paper microfluidics with deep learning for portable intelligent nucleic acid amplification tests

• Published in: Talanta (Oxford) Vol. 258; p. 124470
• Main Authors: Sun, Hao, Xie, Wantao, Huang, Yi, Mo, Jin, Dong, Hui, Chen, Xinkai, Zhang, Zhixing, Shang, Junyi
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.06.2023
• Subjects: Artificial Intelligence; COVID-19 - diagnosis; Deep Learning; Humans; Microfluidics; NAAT; Nucleic Acid Amplification Techniques; Paper microfluidics; SARS-CoV-2 - genetics; Sensitivity and Specificity; Deep learning; COVID-19 diagnosis; NAAT; Paper microfluidics
• ISSN: 0039-9140; 1873-3573
• DOI: 10.1016/j.talanta.2023.124470

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 (Cq) 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. [Display omitted] •Paper device with deep learning enable portable and rapid nucleic acid amplification.•Accurate prediction can be obtained using partly gathered real-time data.•Deep learning models used by on-chip tests were well-suited to clinical study.•Approach can inspire ample innovations in fields of analytical science.