Comprehensive quantitative analysis of erythrocytes and leukocytes using trace volume of human blood using microfluidic-image cytometry and machine learning

) rectangular microchannel, allowing the analysis of trace volume of blood (20 μL) for each assay. Automated analysis of digitized binary images applying a border following algorithm was performed allowing the qualitative analysis of erythrocytes. Bright-field imaging was used for the detection of erythrocytes and fluorescence imaging for 5-part differentiation of leukocytes after acridine orange staining, applying a convolutional neural network enabling unparalleled speed for identification and automated morphology classification yielding 98.57% accuracy. Blood samples were obtained from 30 volunteers and count values did not significantly differ from data obtained using a commercial automated hematology analyzer.