Flow Cytometry

Flow cytometry is a high-throughput technique used to analyze the properties of individual cells within complex mixtures, primarily for identifying and quantifying different cell populations. Current research heavily emphasizes the application of machine learning, particularly deep learning architectures like graph neural networks and transformers, to automate the analysis process, improve accuracy, and reduce the time-consuming manual gating traditionally required. This automation is crucial for improving the speed and reliability of diagnoses in fields like hematology, particularly in detecting cancers like leukemia and monitoring minimal residual disease, ultimately leading to more efficient and accurate clinical workflows.