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Evaluation of a Semi-Supervised AI Model (ASUS Blade) for Peripheral Blood Film Leukocyte Classification.
Bingwen Eugene Fan1,2,3,4, David Tao Yi Chen5, Chiew Yan Lee1
1Department of Haematology, Tan Tock Seng Hospital, Singapore.
International Journal of Laboratory Hematology
|December 16, 2025
Summary
This study shows that the AI model Blade and the CellaVision DM9600 system closely match manual microscopy (MM) for automated leukocyte classification in peripheral blood film analysis. Blade demonstrates potential for enhancing diagnostic accuracy and integrating into hematology workflows.
Area of Science:
- Hematology
- Artificial Intelligence
- Medical Diagnostics
Background:
- Peripheral blood film (PBF) analysis traditionally uses manual microscopy (MM), which is labor-intensive and prone to inter-observer variability.
- Automated methods are being developed to improve efficiency and consistency in PBF analysis.
Purpose of the Study:
- To evaluate the performance of a semi-supervised AI model, Blade, and a commercial system, CellaVision DM9600, against manual microscopy for automated leukocyte classification.
- To assess the diagnostic accuracy and concordance of these automated systems across various leukocyte subtypes.
Main Methods:
- Digital scanning of PBFs from 168 patients.
- Training the Blade AI model using ResNet34 and RetinaNet architectures on a large dataset of cells.
- Evaluating Blade and CellaVision DM9600 against manual microscopy using concordance correlation coefficient (CCC), Bland-Altman analysis, and regression techniques.
Main Results:
- Both Blade and CellaVision showed high agreement with manual microscopy for leukocyte classification.
- Blade achieved excellent correlation for common leukocytes (e.g., neutrophils, lymphocytes) and demonstrated superior performance for metamyelocytes compared to CellaVision.
- Both systems achieved 100% sensitivity for blasts and reactive lymphocytes, with comparable overall macro-averaged performance.
Conclusions:
- Blade and CellaVision DM9600 are clinically useful automated systems for leukocyte classification, showing strong concordance with manual microscopy.
- Blade's semi-supervised approach offers potential advantages in detecting rare cells and improving diagnostic stability.
- AI-driven tools like Blade have the potential to enhance diagnostic accuracy and streamline hematology workflows.
