A Semi‐Automated Microfluidic Platform Employing Machine Learning Analysis to Study Adhesion Kinetics in Acute Myeloid Leukemia

Current platforms for drug screening typically do not account for the tumor microenvironment. Microfluidic shear flow assays provide a highly sensitive tool for studying tumor cell communication at the single cell level with the microenvironment. Adhesion thereby serves as a functional readout that reflects cellular state, including loss of viability. However, previous platforms required extensive manual handling and time-consuming post-assay analysis. We developed a bright-field microscopy-enabled, semi-automated shear flow platform that combines hardware operation with a machine-learning-based analysis pipeline. The algorithm delivers consistent, high-quality results within minutes with a precision of 98.3% and a recall of 99.1%, indicative of high tracking specificity and object discrimination.