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Related Experiment Video

Updated: Jun 30, 2026

Analyzing Platelet Subpopulations by Multi-color Flow Cytometry
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Published on: June 10, 2025

Associating the phenotypic expression of platelets with disease type through image-based single-cell profiling.

Huidong Wang1, Masako Nishikawa2, Yuqi Zhou1

  • 1Department of Chemistry, The University of Tokyo, Tokyo, 113-0033, Japan.

Thrombosis Research
|June 28, 2026
PubMed
Summary

Researchers developed AI models to classify platelet phenotypes from images, identifying disease-associated signatures. These models can predict thrombotic progression days in advance, aiding early disease detection and risk assessment.

Keywords:
Convolutional neural networkDisease classificationImage-based profilingPlatelet activationPlatelet morphologyThrombosis

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Last Updated: Jun 30, 2026

Analyzing Platelet Subpopulations by Multi-color Flow Cytometry
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Published on: June 10, 2025

Procoagulant Platelet Characterization by Measuring Phosphatidylserine Exposure and Microvesicle Release from Human Purified Platelets
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Procoagulant Platelet Characterization by Measuring Phosphatidylserine Exposure and Microvesicle Release from Human Purified Platelets

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Live-cell Imaging of Platelet Degranulation and Secretion Under Flow
11:42

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Published on: July 10, 2017

Area of Science:

  • Hematology
  • Biomedical Imaging
  • Artificial Intelligence

Background:

  • Platelets are crucial for hemostasis and thrombosis, implicated in various diseases like cardiovascular disorders, infections, and cancer.
  • Disease-associated platelet phenotypes are subtle, transient, and difficult to resolve at the single-cell level, hindering comprehensive study.
  • Current understanding of platelet dysfunction in disease lacks detailed phenotypic characterization.

Purpose of the Study:

  • To develop and validate artificial intelligence (AI) models for classifying platelet phenotypes from high-resolution images.
  • To identify disease-associated platelet signatures using advanced imaging and deep learning techniques.
  • To assess the potential of AI-driven platelet analysis for early disease detection and risk stratification.

Main Methods:

  • Collected whole-blood samples from patients and healthy volunteers.
  • Utilized optofluidic imaging with an optical frequency-division-multiplexed (FDM) microscope and microfluidic chip for high-resolution platelet imaging.
  • Trained convolutional neural network (CNN) models to classify platelet phenotypes across disease categories and performed feature-importance analysis.

Main Results:

  • Developed three CNN models achieving up to 81.3% accuracy in classifying platelet images by disease category.
  • Demonstrated that CNN models could predict thrombotic progression up to 7 days prior to clinical detection through longitudinal analysis.
  • Identified texture-related descriptors as key features driving CNN-based platelet characterization, accounting for 54.5% of importance.

Conclusions:

  • Image-based analysis of circulating platelets can effectively capture disease-associated phenotypic signatures.
  • AI-driven platelet phenotyping shows promise for complementing existing clinical workflows.
  • This approach may enhance prediagnosis and enable early assessment of thrombotic risk.