Matrix spillover remains a challenging issue in flow cytometry analysis, influencing the accuracy of experimental results. Recently, deep neural networks have emerged as novel tools to mitigate matrix spillover effects. AI-mediated approaches leverage advanced algorithms to quantify spillover events and adjust for their consequences on data interpr