Artificial intelligence-enhancement of flow cytometry data accelerates the identification of measurable residual chronic lymphocytic leukemia.

Flow cytometry (FC) is essential for detecting measurable residual disease (MRD) in chronic lymphocytic leukemia (CLL), but its use is limited by the expertise and time required for manual analysis. We developed an artificial intelligence (AI) pipeline, Clustering/Classification of All Events, Dimensionality reduction, Downsampling, and Aberrancy Scaling (CCADDAS), to automatically enhance raw FC files, streamlining CLL MRD detection using a single-tube 10-color panel. FC files from 166 MRD-positive and 61 MRD-negative cases were processed in a cloud environment. Automated steps included error correction (FlowCut), clustering (PARC), dimensionality reduction (UMAP), anomaly detection against negative controls, and cluster-informed downsampling that preserved rare MRD events. A deep neural network trained on expert-defined normal subsets enabled automated gating. AI-enhanced files were analyzed in standard FC software, yielding results highly concordant with conventional expert review (R² = 0.98). Downsampling reduced cellularity by 85% and file size by 78%, while retaining low-level MRD events. An AI-generated aberrancy scale distinguished CLL MRD from background B cells with excellent performance (AUC = 0.98). Manual analysis time decreased from 9.0 to 0.9 min per case (90% reduction). CCADDAS provides a largely unsupervised, software-agnostic method that accelerates and simplifies CLL MRD detection without compromising test performance compared to conventional analysis, enabling broader adoption of FC-based MRD testing.