Revolutionizing chronic lymphocytic leukemia diagnosis: A deep dive into the diverse applications of machine learning
Mohamed Elhadary, Amgad M. Elshoeibi, Ahmed Badr, Basel Elsayed, Omar Metwally, Ahmed Mohamed Elshoeibi, Mervat Mattar, Khalil Al‐Farsi, Salem Al-Shammari, Awni Alshurafa, Mohamed A. Yassin
Abstract
Chronic lymphocytic leukemia (CLL) is a B cell neoplasm characterized by the accumulation of aberrant monoclonal B lymphocytes. CLL is the predominant type of leukemia in Western countries, accounting for 25% of cases. Although many patients remain asymptomatic, a subset may exhibit typical lymphoma symptoms, acquired immunodeficiency disorders, or autoimmune complications. Diagnosis involves blood tests showing increased lymphocytes and further examination using peripheral blood smear and flow cytometry to confirm the disease. With the significant advancements in machine learning (ML) and artificial intelligence (AI) in recent years, numerous models and algorithms have been proposed to support the diagnosis and classification of CLL. In this review, we discuss the benefits and drawbacks of recent applications of ML algorithms in the diagnosis and evaluation of patients diagnosed with CLL.