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