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    Revolutionizing chronic lymphocytic leukemia diagnosis: A deep dive into the diverse applications of machine learning

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    1-s2.0-S0268960X23000954-main.pdf (5.756Mb)
    Date
    2023-11
    Author
    Elhadary, Mohamed
    Elshoeibi, Amgad Mohamed
    Badr, Ahmed
    Elsayed, Basel
    Metwally, Omar
    Elshoeibi, Ahmed Mohamed
    Mattar, Mervat
    Alfarsi, Khalil
    AlShammari, Salem
    Alshurafa, Awni
    Yassin, Mohamed
    ...show more authors ...show less authors
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    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.
    URI
    https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85172075801&origin=inward
    DOI/handle
    http://dx.doi.org/10.1016/j.blre.2023.101134
    http://hdl.handle.net/10576/51324
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    • Medicine Research [‎1794‎ items ]

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