aceComprehensive Multicenter Evaluation of an AI-Based Deep Learning System for Automated Blood Cell Classification and Morphologic Analysis
Background and objectives Deep learning-based systems have shown promise in automating white blood cell (WBC) classification; however, most validations are limited to single institutions. This study aimed to evaluate the multicenter performance and robustness of a CNN-based deep learning system (DLS) for peripheral blood cell classification and morphologic abnormality detection across three hospitals. Methods A total of 766 peripheral blood smears (613 routine and 153 abnormal) from Juntendo University Hospital,…
A CNN-based system analyzing 766 blood smears at three hospitals classified white blood cells with over 95% accuracy and flagged smears with blasts or abnormal cells for expert review.
Agreement with manual microscopy was only moderate for monocytes and basophils, so the system's counts for these cell types varied more than for other white blood cell types.
Agreement with manual microscopy was only moderate for monocytes and basophils, so performance is not uniform across all cell types, and the abstract reports no patient outcome data.
Evidence
- Peer-reviewedInternational Journal of Laboratory Hematology2026-10-09
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Truvace Impact Record TRV-2026-1603, v1: “Comprehensive Multicenter Evaluation of an AI-Based Deep Learning System for Automated Blood Cell Classification and Morphologic Analysis.” Truvace, 2026-10-16. /record/TRV-2026-1603 (accessed at citation time). sha256 9e7149ecd2d091a3…
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