Red blood cell (RBC) deformability is a key determinant of microcirculatory flow and can be altered in hematological disorders such as chronic lymphocytic leukemia (CLL). This study aimed to evaluate RBC deformability under controlled microfluidic flow conditions and to assess the influence of software platform choice on deformability quantification. RBC suspensions from healthy individuals and untreated CLL patients were analyzed using a microfluidic imaging system across a range of shear rates. A dedicated image-processing algorithm was developed and implemented in two software environments (LabVIEW and Python) to automatically detect deformed cells, measure major and minor cell axes, and calculate the deformability index (DI). Both analytical approaches demonstrated a shear-dependent increase in DI in healthy controls, whereas RBCs from CLL patients exhibited reduced deformability and a blunted response to increasing shear rates, particularly at intermediate shear rates. Although LabVIEW produced consistently higher absolute DI values than Python, both platforms showed strong correlation and preserved the same relative trends and group discrimination. These findings demonstrate that microfluidic image flow analysis provides a robust approach for assessing RBC biomechanics and highlight the importance of standardized image-processing workflows for reliable deformability quantification across software platforms.

Red blood cell (RBC) deformability is a key determinant of microcirculatory flow and can be altered in hematological disorders such as chronic lymphocytic leukemia (CLL). This study aimed to evaluate RBC deformability under controlled microfluidic flow conditions and to assess the influence of software platform choice on deformability quantification. RBC suspensions from healthy individuals and untreated CLL patients were analyzed using a microfluidic imaging system across a range of shear rates. A dedicated image-processing algorithm was developed and implemented in two software environments (LabVIEW and Python) to automatically detect deformed cells, measure major and minor cell axes, and calculate the deformability index (DI). Both analytical approaches demonstrated a shear-dependent increase in DI in healthy controls, whereas RBCs from CLL patients exhibited reduced deformability and a blunted response to increasing shear rates, particularly at intermediate shear rates. Although LabVIEW produced consistently higher absolute DI values than Python, both platforms showed strong correlation and preserved the same relative trends and group discrimination. These findings demonstrate that microfluidic image flow analysis provides a robust approach for assessing RBC biomechanics and highlight the importance of standardized image-processing workflows for reliable deformability quantification across software platforms.