Applications of Artificial Intelligence in Thrombocytopenia

Amgad M. Elshoeibi*, Khaled Ferih, Ahmed Adel Elsabagh, Basel Elsayed, Mohamed Elhadary, Mahmoud Marashi, Yasser Wali, Mona Al-Rasheed, Murtadha Al-Khabori, Hani Osman, Mohamed Yassin*

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

6 Citations (Scopus)


Thrombocytopenia is a medical condition where blood platelet count drops very low. This drop in platelet count can be attributed to many causes including medication, sepsis, viral infections, and autoimmunity. Clinically, the presence of thrombocytopenia might be very dangerous and is associated with poor outcomes of patients due to excessive bleeding if not addressed quickly enough. Hence, early detection and evaluation of thrombocytopenia is essential for rapid and appropriate intervention for these patients. Since artificial intelligence is able to combine and evaluate many linear and nonlinear variables simultaneously, it has shown great potential in its application in the early diagnosis, assessing the prognosis and predicting the distribution of patients with thrombocytopenia. In this review, we conducted a search across four databases and identified a total of 13 original articles that looked at the use of many machine learning algorithms in the diagnosis, prognosis, and distribution of various types of thrombocytopenia. We summarized the methods and findings of each article in this review. The included studies showed that artificial intelligence can potentially enhance the clinical approaches used in the diagnosis, prognosis, and treatment of thrombocytopenia.
Original languageEnglish
Article number1060
Issue number6
Publication statusPublished - Mar 10 2023


  • artificial intelligence
  • diagnosis
  • prediction
  • prognosis
  • thrombocytopenia
  • transmission

ASJC Scopus subject areas

  • Clinical Biochemistry

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