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Single-cell Analysis of Immunophenotype and Cytokine Production in Peripheral Whole Blood via Mass Cytometry
Published on: June 26, 2018
AI-Enabled Automated Schistocyte Classification in Peripheral Blood for TMA Auxiliary Diagnosis
Lei Shang1, Chao Fang2, Dongshuo Li1
1Department of Clinical Laboratory, Aerospace Center Hospital, Beijing, China.
Background:
Schistocytes are critical morphological markers for thrombotic microangiopathy (TMA) diagnosis. Manual identification is hampered by inconsistent standards and high interobserver variability, compromising accuracy. Although AI has advanced in hematology, AI-enabled schistocyte classification remains under-studied, creating a clinical gap.
Methods:
We constructed a two-stage AI system for segmentation and classification. Three segmentation models (U-Net, ENet, and R2U-Net) were assessed on 25 067 RBCs from 183 patients. ResNet-50 and Xception were trained on 28 586 RBCs, including 13 125 ICSH-classified schistocytes, using grayscale and RGB images. Clinical validation was performed on 156 784 RBCs from 219 patients. Performance was evaluated using recall, specificity, precision, and F1 score.
Results:
R2U-Net achieved the best segmentation (recall = 0.868, F1 = 0.881, mIoU = 0.807). The Xception-RGB model performed best in classification (weighted F1 = 0.957), with high precision and recall for schistocyte subtypes. Clinical validation showed excellent reliability with all weighted metrics at 0.998.
Conclusions:
This two-stage framework enables accurate schistocyte analysis with better feature representation using RGB images and Xception. It improves diagnostic accuracy and reproducibility, supporting TMA auxiliary diagnosis and clinical decision-making.