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