AI-based detection of neutrophil dysplasia: an accessible and sensitive model for MDS diagnosis from peripheral blood.

ABSTRACT: Myelodysplastic syndrome / neoplasm (MDS) presents a diagnostic challenge due to the need of expert morphological analysis, and the reliance on genomic analysis of collected bone marrow material for the definite diagnosis. This study aimed to facilitate this process by developing a computer vision AI-based model that is capable of diagnosing MDS from images from peripheral blood smears (PBS). We used a cohort of 43,371 neutrophils from 84 MDS and 60 non-MDS samples to train a neutrophil classifier to differentiate between dysplastic and non-dysplastic cells. The model was initially fed with PBS images from patients with prominent MDS (pMDS) and further refined to detect non-prominent MDS (npMDS), i.e., without clear-cut dysplastic features in their neutrophils. The model learning was only

SUBMITTER: Romano NH 

PROVIDER: S-EPMC12552336 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature