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