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

Mohamed Elhadary; Amgad Mohamed Elshoeibi; Ahmed Badr; Basel Elsayed; Omar Metwally; Ahmed Mohamed Elshoeibi; Mervat Mattar; Khalil Alfarsi; Salem AlShammari; Awni Alshurafa; Mohamed Yassin

doi:10.1016/j.blre.2023.101134

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

Blood Rev. 2023 Nov:62:101134. doi: 10.1016/j.blre.2023.101134. Epub 2023 Sep 22.

Authors

Affiliations

¹ College of Medicine, QU Health, Qatar University, Doha, Qatar. Electronic address: [email protected].
² College of Medicine, QU Health, Qatar University, Doha, Qatar.
³ School of Medicine, Newgiza University, Giza, Egypt.
⁴ Internal Medicine and Clinical Hematology, Cairo University, Cairo, Egypt.
⁵ Department of Hematology, College of Medicine and Health Sciences, Sultan Qaboos University, Muscat, Oman.
⁶ Department of Medicine, Faculty of Medicine, Kuwait University, Kuwait, Kuwait.
⁷ Hematology Section, Medical Oncology, National Center for Cancer Care and Research (NCCCR), Hamad Medical Corporation, Doha, Qatar.
⁸ Hematology Section, Medical Oncology, National Center for Cancer Care and Research (NCCCR), Hamad Medical Corporation, Doha, Qatar. Electronic address: [email protected].

PMID: 37758527
DOI: 10.1016/j.blre.2023.101134

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.

Keywords: Artificial intelligence; Chronic lymphocytic leukemia; Diagnosis; Machine learning.

Publication types

Review
Research Support, Non-U.S. Gov't

MeSH terms

Artificial Intelligence
B-Lymphocytes / pathology
Humans
Leukemia, Lymphocytic, Chronic, B-Cell* / diagnosis
Leukemia, Lymphocytic, Chronic, B-Cell* / pathology
Lymphoma* / pathology
Machine Learning