Intelligent classification of platelet aggregates by agonist type

Yuqi Zhou, Atsushi Yasumoto, Cheng Lei, Chun Jung Huang, Hirofumi Kobayashi, Yunzhao Wu, Sheng Yan, Chia Wei Sun, Yutaka Yatomi, Keisuke Goda

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


Platelets are anucleate cells in blood whose principal function is to stop bleeding by forming aggregates for hemostatic reactions. In addition to their participation in physiological hemostasis, platelet aggregates are also involved in pathological thrombosis and play an important role in inflammation, atherosclerosis, and cancer metastasis. The aggregation of platelets is elicited by various agonists, but these platelet aggregates have long been considered indistinguishable and impossible to classify. Here we present an intelligent method for classifying them by agonist type. It is based on a convolutional neural network trained by high-throughput imaging flow cytometry of blood cells to identify and differentiate subtle yet appreciable morphological features of platelet aggregates activated by different types of agonists. The method is a powerful tool for studying the underlying mechanism of platelet aggregation and is expected to open a window on an entirely new class of clinical diagnostics, pharmacometrics, and therapeutics.

Original languageEnglish
StatePublished - 12 May 2020


  • blood
  • cell biology
  • deep learning
  • human
  • human biology
  • imaging flow cytometry
  • medicine
  • microfluidics
  • platelet
  • thrombosis

Fingerprint Dive into the research topics of 'Intelligent classification of platelet aggregates by agonist type'. Together they form a unique fingerprint.

Cite this