Assessment of anemia recovery using peripheral blood smears by deep semi-supervised learning.

ABSTRACT: Monitoring anemia recovery is crucial for clinical intervention. Morphological assessment of red blood cells (RBCs) with peripheral blood smears (PBSs) provides additional information beyond routine blood tests. However, the PBS test is labor-intensive, reliant on manual analysis, and susceptible to variability in expert interpretations. Here we introduce a deep semi-supervised learning method, RBCMatch, to classify RBCs during anemia recovery. Using an acute hemolytic anemic mouse model, PBS images at four different time points during anemia recovery were acquired and segmented into 10,091 single RBC images, with only 5% annotated and used in model training. By employing the semi-supervised strategy Fixmatch, RBCMatch achieved an impressive average classification accuracy of 91.2% on the

SUBMITTER: Yan Q 

PROVIDER: S-EPMC12031976 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature