Matrix spillover remains a significant issue in flow cytometry analysis, influencing the reliability of experimental results. Recently, deep neural networks have emerged as promising tools to mitigate matrix spillover effects. AI-mediated approaches leverage advanced algorithms to quantify spillover events and correct for their influence on data in