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Updated: Jun 12, 2025
06:47
Microfluidics in Assessing Platelet Function
Published on: November 8, 2024
801
Exploring the performance of an artificial intelligence- and morphology-driven workflow integrating 4 platelet
Julien Guy1, Marie-C Béné2, Ramon Simon Lopez3
1Hematology Biology Department, Dijon University Hospital, Dijon, France.
American Journal of Clinical Pathology
|June 10, 2025
Summary
A new Mindray CAL-8000 platform algorithm accurately evaluated platelet counts in 100% of samples. This method overcomes challenges like EDTA-induced pseudo-thrombocytopenia (PTCP) for reliable patient management.
Area of Science:
- Hematology
- Clinical Pathology
- Medical Diagnostics
Background:
- Accurate platelet counting is crucial for patient management.
- Platelet counts can be inaccurately affected by EDTA-induced pseudo-thrombocytopenia (PTCP), microcytic red blood cells, RBC fragments, or giant platelets.
- The Mindray CAL-8000 platform offers a novel, multi-method approach for platelet enumeration.
Purpose of the Study:
- To evaluate a new set of 4 methods on the Mindray CAL-8000 platform for accurate platelet counting.
- To assess the platform's ability to provide reliable platelet counts from a single EDTA sample, even in the presence of common interfering factors.
- To validate a new algorithm combining impedance, optical, and morphology-based methods for routine clinical use.
Main Methods:
- Evaluation of four platelet counting methods: impedance (PLT-I), optical with disaggregating agent (PLT-O), AI-aided morphology (PLT-M), and extended morphology (PLT-Pro).
- Analysis of 2474 routine EDTA-collected samples on the Mindray CAL-8000 platform.
- Application of a predefined algorithm to combine results from the four methods for a final platelet count.
Main Results:
- The integrated algorithm successfully provided an automated report with accurate platelet evaluation for 100% of the tested samples.
- The combined methods effectively managed challenges such as EDTA-induced pseudo-thrombocytopenia (PTCP) and other interfering factors.
- The sequence of PLT-I, PLT-O, PLT-M, and PLT-Pro enabled accurate platelet counting even with challenging sample characteristics.
Conclusions:
- The evaluated algorithm, utilizing a sequence of four distinct platelet counting methods, demonstrated high accuracy and reliability.
- This proof-of-concept study validated a novel approach for routine platelet count analysis, adaptable for clinical implementation.
- The Mindray CAL-8000 platform's multi-parameter approach offers a robust solution for overcoming common interferences in platelet enumeration.
