LAUSR

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.