  Blood rheology and microcirculation are strongly influenced by red blood cell
aggregation. We investigate the dissociation rates of red cell aggregates in
extensional flow using hyperbolic microfluidic constrictions and image analysis
by a convolutional neural network (CNN). Our findings reveal that aggregate
dissociation increases sharply when a critical extension rate is reached which
falls within the range of microcirculatory conditions, suggesting that large
variations of aggregate sizes should be expected in-vivo. This work contributes
to a deeper understanding of the behavior of red blood cell aggregates in
response to extensional stress in microcirculatory networks, provides crucial
experimental data to validate theoretical and numerical models, and constitutes
the basis for improved evaluation of blood aggregability in clinical contexts.
