Microfluidics with Machine Learning for Biophysical Characterization of Cells

Understanding the biophysical properties of cells is essential for biological research, diagnostics, and therapeutics. Microfluidics enhances biophysical cell characterization by enabling precise manipulation and real-time measurement at the microscale. However, the high-throughput nature of microfluidic systems generates vast amounts of data, complicating analysis. Integrating artificial intelligence (AI) methods, including machine learning and deep learning, with microfluidic technologies addresses these challenges. AI excels at analyzing large, complex datasets, improving the accuracy and efficiency of microfluidic experiments and facilitating new biological discoveries. This review examines the synergy between microfluidics and machine learning for biophysical cell characterization, categorizing existing methods based on the types of input data used for machine learning analysis, highlighting recent advancements, and discussing challenges and future directions in this interdisciplinary field.