Deep learning-assisted 10-μL single droplet-based viscometry for human aqueous humor

Probing the viscosity of human aqueous humor is crucial for optimizing micro-tube shunts in glaucoma treatment. However, conventional viscometers are not suitable for aqueous humor due to the limited sample volume-only tens of microliters-that can be safely extracted without causing permanent ocular damage. Here, we present an artificial intelligence-assisted microfluidic viscometry for measuring 10-μL aqueous humor collected at the point of care. Our approach involves injecting a single droplet of the sample into a microfluidic chip using hydrostatic pressure, minimizing interfacial effects with surfactants and hydrophobic coatings, and analyzing the sample flow using a deep learning-based detection scheme. For the first time, we have measured the viscosity of a 10-μL human aqueous humor and observed approximately 30 % variation between individuals. These individual differences in aqueous humor viscosity should be considered when designing microtube shunts for glaucoma treatment. Our method paves the way for the viscometry of small-volume biofluids, enabling new diagnostic and therapeutic applications in biomedical technology.