posted on 2022-02-18, 14:13authored byNabil
H. Bhuiyan, Jun H. Hong, M. Jalal Uddin, Joon S. Shim
There
have been tremendous innovations in microfluidic clinical
diagnostics to facilitate novel point-of-care testing (POCT) over
the past decades. However, the automatic operation of microfluidic
devices that minimize user intervention still lacks reliability and
repeatability because microfluidic errors such as bubbles and incomplete
filling pose a major bottleneck in commercializing the microfluidic
devices for clinical testing. In this work, for the first time, various
states of microfluid were recognized to control immunodiagnostics
by artificial intelligence (AI) technology. The developed AI-controlled
microfluidic platform was operated via an Android smartphone, along
with a low-cost polymer device to effectuate enzyme-linked immunosorbent
assay (ELISA). To overcome the limited machine-learning capability
of smartphones, the region-of-interest (ROI) cascading and conditional
activation algorithms were utilized herein. The developed microfluidic
chip was incorporated with a bubble trap to remove any bubbles detected
by AI, which helps in preventing false signals during immunoassay,
as well as controlling the reagents’ movement with an on-chip
micropump and valve. Subsequently, the developed immunosensing platform
was tested for conducting real ELISA using a single microplate from
the 96-well to detect the Human Cardiac Troponin I (cTnI) biomarker,
with a detection limit as low as 0.98 pg/mL. As a result, the developed
platform can be envisaged as an AI-based revolution in microfluidics
for point-of-care clinical diagnosis.