Deep Learning-based Kinetic Analysis in Paper-based Analytical Cartridges Integrated with Field-effect Transistors

This study explores the fusion of a field-effect transistor (FET), a paper-based analytical cartridge, and the computational power of deep learning (DL) for quantitative biosensing via kinetic analyses. The FET sensors address the low sensitivity challenge observed in paper analytical devices, enabl...

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Bibliographic Details
Published in:arXiv.org 2024-02
Main Authors: Hyun-June Jang, Hyou-Arm Joung, Goncharov, Artem, Anastasia Gant Kanegusuku, Chan, Clarence W, Kiang-Teck, Jerry Yeo, Zhuang, Wen, Ozcan, Aydogan, Chen, Junhong
Format: Article
Language:English
Subjects:
Biosensors
Cartridges (explosive)
Coefficient of variation
Cost analysis
Deep learning
Economic analysis
Electrical measurement
Field effect transistors
Mathematical analysis
Semiconductor devices
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Summary:This study explores the fusion of a field-effect transistor (FET), a paper-based analytical cartridge, and the computational power of deep learning (DL) for quantitative biosensing via kinetic analyses. The FET sensors address the low sensitivity challenge observed in paper analytical devices, enabling electrical measurements with kinetic data. The paper-based cartridge eliminates the need for surface chemistry required in FET sensors, ensuring economical operation (cost < $0.15/test). The DL analysis mitigates chronic challenges of FET biosensors such as sample matrix interference, by leveraging kinetic data from target-specific bioreactions. In our proof-of-concept demonstration, our DL-based analyses showcased a coefficient of variation of < 6.46% and a decent concentration measurement correlation with an r2 value of > 0.976 for cholesterol testing when blindly compared to results obtained from a CLIA-certified clinical laboratory. These integrated technologies can create a new generation of FET-based biosensors, potentially transforming point-of-care diagnostics and at-home testing through enhanced accessibility, ease-of-use, and accuracy.
ISSN:2331-8422