  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.
