AI-Enabled Automated Schistocyte Classification in Peripheral Blood for TMA Auxiliary Diagnosis.

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.