FlowFATE: New project uses AI to improve leukemia treatment monitoring
The Vienna Business Agency has awarded a research grant to Margarita Maurer-Granofszky, Staff Scientist in Michael Dworzak’s lab, to develop improved flow cytometry-based diagnostics in leukemia using AI.
In Simple Terms
– Detecting even very small numbers of leukemic cells in a patient’s blood is essential to monitor and adjust treatment over time.
– This requires complex and time-intensive data analysis by trained experts, and small differences can influence the treatment patients receive.
– FlowFATE uses AI to help scientists analyze this complex data to more easily identify leukemia cells and make better decisions.
In childhood leukemia, treatment decisions depend on detecting tiny numbers of cancer cells that remain after therapy. This minimal residual disease (MRD) is an important indicator of how well treatment is working and can help doctors adjust therapy accordingly.
MRD can be measured using flow cytometry, a highly sensitive technology that analyses large numbers of cells in individual patient samples. The challenge lies in identifying the few remaining leukemic cells among hundreds of thousands of healthy cells.
“Identifying a few hundred—or even as few as ten—leukemic cells within such a large population requires an expert eye and significant training,” explains Margarita Maurer-Granofszky. “When the assessment depends on such small numbers of cells, even minor differences in MRD analysis can influence treatment decisions.”
AI improves MRD data analysis
The group of Michael Dworzak at St. Anna CCRI has been working for years on approaches to make flow cytometry analysis more standardized, reproducible and less dependent on individual interpretation. In the new project FlowFATE, which has received funding through the Vienna Business Agency’s Tech4People 2026 funding call, the team aims to develop an AI-based tool that can automatically identify leukemic cells in flow cytometry data.
“This project builds upon our long-standing expertise in automated diagnostics,” explains Michael Dworzak. “Together with collaborators at the Technical University of Vienna, we’re combining extensive patient-derived flow cytometry data with advanced AI models to develop a robust automated tool to assess MRD in children with leukemia.”
The technology could transform an expertise-intensive and time-consuming analysis into a more standardized automated process. By improving reproducibility and supporting precise MRD assessment, FlowFATE could help clinicians make well-informed treatment decisions, ensuring that every patient receives the most accurate treatment.
The project may also have applications beyond childhood leukemia. “We want to develop a model that can eventually be adapted to other applications in flow cytometry,” says Margarita Maurer-Granofszky. “By adjusting it to different tasks, FlowFATE could help scientists and doctors identify other relevant cell populations in flow cytometry data and potentially expand the use of automated analysis to additional diseases.”
About Tech4People
Tech4People is a funding program by the Vienna Business Agency (Wirtschaftsagentur Wien) that supports research and development projects combining digital technology with human-centric and ethical values. The program provides substancial grants for application-oriented research and experimental development, focusing on the creation or improvement of products, services, or processes where technology follows people, and not people follow technology.