B Acute Lymphoblastic Leukemia (B-ALL) accounts for approximately 80% of pediatric leukemia cases. Despite treatment advances, 15–20% of children experience relapse, highlighting the need of improved monitoring of patients and novel strategies leading to successful therapies. Flow Cytometry is an essential technique for measuring residual disease and guiding treatment. However, traditional manual gating limits its efficiency. In recent years, computational tools have been integrated to enhance these clinical processes but many mathematical techniques are underexploited. In a research work published in the journal BioData Mining, MOLAB researchers from University of Cadiz together with clinicians from Hospital del Niño Jesús (Madrid) exploited 234 samples from 75 B-ALL patients to develop an artificial intelligence-based algorithm that can improve patient classification and therapy decisions in different patient cohorts. The findings imply a substantial advancement on the routine manual analysis of the disease progression, as the researchers identified key subpopulations automatically, distinguishing patients’ bone marrow regeneration patterns, thus improving the prediction and prognosis asessment of the disease.

Scientific paper link

https://biodatamining.biomedcentral.com/articles/10.1186/s13040-025-00488-z