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