Citizen Science Gamers Enable Automated Flow Cytometry Gating Through Machine Learning

Machine learning can greatly accelerate biomedical research but is fundamentally constrained by the scarcity of high‐quality labeled data. We introduce a scalable data‐generation framework based on citizen science and apply it to automated flow cytometry gating, a foundational but subjective step in immunology that limits reproducibility. Using the massively multiplayer online game EVE Online, 839,199 de‐identified player accounts analyzed 52,000 plots, from which stringent quality control produced 31,703 consensus annotations for model training. Leveraging these data, we developed flowMagic, a machine learning framework for automated gating. Evaluated on 92,203 bivariate plots with standardized reference gates spanning 79 immune populations, the template model outperformed the generalized model, achieving 90% and 65% agreement for large and low‐abundance populations, respectively, compared with 78% and 42% for the generalized model. These results show that expert‐provided templates currently provide the highest agreement, while the citizen‐science‐trained generalized model enables gating without gate‐specific templates but remains limited by the absence of expert‐curated examples for complex, low‐abundance or context‐dependent gates. Both model types reproduced biologically meaningful patterns, including neutrophil dynamics in COVID‐19 patients and immune development in newborns. Together, these results establish citizen‐science annotation as a scalable strategy for generating training data that complements, rather than replaces, expert input in automated flow cytometry gating.