Applications of Artificial Intelligence in Thrombocytopenia

Abstract

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.

Keywords: artificial intelligence; diagnosis; prediction; prognosis; thrombocytopenia; transmission.

Figure 1

Schematic representation of review process.

Figure 2

AUC for ML models predicting severe thrombocytopenia: (A) internal validation and (B) external validation (Jiang, X. et al., 2022) [11].

Figure 3

Classification tree for thrombocytopenia prediction. PLT96h: Platelet change from the baseline at 96 h after the initial dose. Cmin96 h: linezolid total concentration at 96 h after the initial dose (Takahashi, S. et al., 2021) [13].

Figure 4

Proposed diagnostic algorithm for heparin induced thrombocytopenia: TORADI−HITP (Nilius, H. et al., 2022) [15].

Figure 5

Schematic diagram for model predictions (Cho, G., S. Lee, and H. Lee 2021) [19].