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 validation dataset and 87.5% on a held-out dataset, demonstrating its superior accuracy and robustness compared to supervised learning methods, especially when labeled samples are scarce. To characterize the anemia recovery process, principal components (PCs) of RBC embeddings were extracted and visualized. Our results indicated that RBC embeddings quantified the state of anemia recovery, and the second PC had a strong correlation with RBC count and hemoglobin concentration, demonstrating the model's ability to accurately depict RBC morphological changes during anemia recovery. Thus, this study provides a valuable tool for the automatic classification of RBCs and offers novel insights into the assessment of anemia recovery, with the potential to aid in clinical decision-making and prognosis analysis in the future.