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