Machine learning applications in the diagnosis of leukemia: Current trends and future directions. (9th September 2019)

• Record Type: Journal Article
• Authors: Salah, Haneen T.; Muhsen, Ibrahim N.; Salama, Mohamed E.; Owaidah, Tarek; Hashmi, Shahrukh K.
• Is Part Of: International journal of laboratory hematology. Volume 41:Number 6(2019:Dec.)
• Page Start: 717
• Page End: 725
• Publication Date: 2019-09-09
• Subjects: diagnosis -- digital -- leukemia -- machine learning -- pathology; Hematology -- Periodicals; Blood -- Diseases -- Periodicals; Hematology -- Periodicals; 616.15005
• DOI: 10.1111/ijlh.13089
• Languages: English
• ISSNs: 1751-5521

Abstract

Abstract: Machine learning (ML) offers opportunities to advance pathological diagnosis, especially with increasing trends in digitalizing microscopic images. Diagnosing leukemia is time‐consuming and challenging in many areas globally and there is a growing trend in utilizing ML techniques for its diagnosis. In this review, we aimed to describe the literature of ML utilization in the diagnosis of the four common types of leukemia: acute lymphocytic leukemia (ALL), chronic lymphocytic leukemia (CLL), acute myeloid leukemia (AML), and chronic myelogenous leukemia (CML). Using a strict selection criterion, utilizing MeSH terminology and Boolean logic, an electronic search of MEDLINE and IEEE Xplore Digital Library was performed. The electronic search was complemented by handsearching of references of related studies and the top results of Google Scholar. The full texts of 58 articles were reviewed, out of which, 22 studies were included. The number of studies discussing ALL, AML, CLL, and CML was 12, 8, 3, and 1, respectively. No studies were prospectively applying algorithms in real‐world scenarios. Majority of studies had small and homogenous samples and used supervised learning for classification tasks. 91% of the studies were performed after 2010, and 74% of the included studies applied ML algorithms to microscopic diagnosis of leukemia. The included studies illustrated the need to develop the field of ML research, including the transformation from solely designing algorithms to practically applying them clinically.