Applications of Artificial Intelligence in Thrombocytopenia

Area of Science:

• Medical Informatics
• Hematology
• Artificial Intelligence

Background:

• Thrombocytopenia, characterized by a critically low platelet count, poses significant health risks due to potential excessive bleeding.
• Early detection and evaluation are crucial for timely intervention and improved patient outcomes in thrombocytopenia.
• Various factors, including medications, sepsis, viral infections, and autoimmunity, can cause thrombocytopenia.

Purpose of the Study:

• To review the application of machine learning algorithms in the diagnosis, prognosis, and patient distribution of thrombocytopenia.
• To summarize the methodologies and findings of studies utilizing artificial intelligence for thrombocytopenia management.
• To assess the potential of artificial intelligence in enhancing clinical approaches for thrombocytopenia.

Main Methods:

• A systematic literature search was conducted across four databases.
• 13 original articles focusing on machine learning applications for thrombocytopenia were identified and reviewed.
• Methods and findings from included studies were summarized.

Main Results:

• Studies demonstrated that artificial intelligence can effectively analyze complex variables for diagnosing thrombocytopenia.
• Machine learning models showed potential in assessing patient prognosis and predicting disease distribution.
• The review highlighted the capability of AI to enhance early detection and evaluation of thrombocytopenia.

Conclusions:

• Artificial intelligence holds significant potential to improve the clinical diagnosis of thrombocytopenia.
• AI-driven tools can aid in predicting prognosis and guiding treatment strategies for patients with low platelet counts.
• The integration of machine learning can enhance the overall management and patient outcomes in thrombocytopenia.