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