{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T00:52:11Z","timestamp":1787014331376,"version":"build-2736575974"},"reference-count":56,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2019,9,9]],"date-time":"2019-09-09T00:00:00Z","timestamp":1567987200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Int J Lab Hematology"],"published-print":{"date-parts":[[2019,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Machine learning (ML) offers opportunities to advance pathological diagnosis, especially with increasing trends in digitalizing microscopic images. Diagnosing leukemia is time\u2010consuming 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\u2010world 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.<\/jats:p>","DOI":"10.1111\/ijlh.13089","type":"journal-article","created":{"date-parts":[[2019,9,9]],"date-time":"2019-09-09T12:44:45Z","timestamp":1568033085000},"page":"717-725","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":113,"title":["Machine learning applications in the diagnosis of leukemia: Current trends and future directions"],"prefix":"10.1111","volume":"41","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2676-0473","authenticated-orcid":false,"given":"Haneen T.","family":"Salah","sequence":"first","affiliation":[{"name":"College of Medicine Alfaisal University Riyadh Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ibrahim N.","family":"Muhsen","sequence":"additional","affiliation":[{"name":"Department of Medicine Houston Methodist Hospital Houston TX USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohamed E.","family":"Salama","sequence":"additional","affiliation":[{"name":"Department of Laboratory Medicine &amp; Pathology Mayo Clinic Rochester MN USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tarek","family":"Owaidah","sequence":"additional","affiliation":[{"name":"Department of Pathology and Laboratory Medicine King Faisal Specialist Hospital and Research Center Riyadh Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shahrukh K.","family":"Hashmi","sequence":"additional","affiliation":[{"name":"Oncology Center King Faisal Specialist Hospital and Research Center Riyadh Saudi Arabia"},{"name":"Division of Hematology, Department of Medicine Mayo Clinic Rochester MN USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2019,9,9]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2017.18391"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-018-0300-7"},{"key":"e_1_2_8_4_1","volume-title":"U.S. Food and Drug Administration","author":"Digital Health Criteria [Internet]","year":"2019"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2017.14580"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2017.07.005"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.leukres.2010.01.005"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-pathol-011811-120902"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1001\/jamaoncol.2017.5449"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.4103\/jpi.jpi_53_18"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2017.14585"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-018-0177-5"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1038\/nature26000"},{"key":"e_1_2_8_14_1","volume-title":"GLOBOCAN 2012 v1.0. 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