{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T07:35:48Z","timestamp":1791876948794,"version":"4.3.4"},"reference-count":51,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2025,5,21]],"date-time":"2025-05-21T00:00:00Z","timestamp":1747785600000},"content-version":"vor","delay-in-days":20,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["HemaSphere"],"published-print":{"date-parts":[[2025,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Immunophenotypic detection and quantification of residual leukemic cells by multiparameter flow cytometry is increasingly adopted in the clinical practice of acute myeloid leukemia (AML) to assess measurable residual disease (MRD). However, MRD levels quantified by manual gating analysis can differ based on differences in gating strategy between trained operators and clinical centers. Manual gating requires extensive training, is time\u2010consuming in daily practice, and faces a significant hurdle in analyzing data from next\u2010generation cytometry platforms. To address these challenges, several computational approaches involving machine learning and artificial intelligence algorithms have been proposed to automate or aid the assessment of MRD. However, the immunophenotypic variability between patients and the relatively low proportions of residual leukemic cells in AML challenge most algorithms and require innovative approaches. This review provides an overview of recent efforts in using computational methods for immunophenotypic AML\u2010MRD assessment. We first explain the technical and conceptual background of the different algorithms that have been explored. Next, we discuss their strengths and limitations in the disease\u2010specific context of AML. Finally, we highlight how computational approaches offer a unique opportunity to standardize or even outperform current manual gating analyses,\u00a0and ultimately, improve the treatment of AML patients.<\/jats:p>","DOI":"10.1002\/hem3.70138","type":"journal-article","created":{"date-parts":[[2025,5,21]],"date-time":"2025-05-21T05:06:03Z","timestamp":1747803963000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Applications of machine learning for immunophenotypic measurable residual disease assessment in acute myeloid leukemia"],"prefix":"10.1002","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9170-4131","authenticated-orcid":false,"given":"Tim R.","family":"Mocking","sequence":"first","affiliation":[{"name":"Department of Hematology, Amsterdam UMC Vrije Universiteit Amsterdam Amsterdam The Netherlands"},{"name":"Cancer Center Amsterdam Imaging and Biomarkers Amsterdam The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8311-983X","authenticated-orcid":false,"given":"Arjan A.","family":"van de Loosdrecht","sequence":"additional","affiliation":[{"name":"Department of Hematology, Amsterdam UMC Vrije Universiteit Amsterdam Amsterdam The Netherlands"},{"name":"Cancer Center Amsterdam Imaging and Biomarkers Amsterdam The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9150-8026","authenticated-orcid":false,"given":"Jacqueline","family":"Cloos","sequence":"additional","affiliation":[{"name":"Department of Hematology, Amsterdam UMC Vrije Universiteit Amsterdam Amsterdam The Netherlands"},{"name":"Cancer Center Amsterdam Imaging and Biomarkers Amsterdam The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5983-6193","authenticated-orcid":false,"given":"Costa","family":"Bachas","sequence":"additional","affiliation":[{"name":"Department of Hematology, Amsterdam UMC Vrije Universiteit Amsterdam Amsterdam The Netherlands"},{"name":"Cancer Center Amsterdam Imaging and Biomarkers Amsterdam The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,5,21]]},"reference":[{"key":"e_1_2_12_2_1","doi-asserted-by":"publisher","DOI":"10.1001\/jamaoncol.2020.4600"},{"key":"e_1_2_12_3_1","doi-asserted-by":"publisher","DOI":"10.1182\/blood.2022016867"},{"key":"e_1_2_12_4_1","doi-asserted-by":"publisher","DOI":"10.1002\/cpcy.66"},{"key":"e_1_2_12_5_1","doi-asserted-by":"publisher","DOI":"10.1002\/cpcy.73"},{"key":"e_1_2_12_6_1","doi-asserted-by":"publisher","DOI":"10.3390\/ijms25094881"},{"key":"e_1_2_12_7_1","doi-asserted-by":"publisher","DOI":"10.1038\/leu.2015.313"},{"key":"e_1_2_12_8_1","doi-asserted-by":"publisher","DOI":"10.1038\/leu.2017.29"},{"key":"e_1_2_12_9_1","doi-asserted-by":"publisher","DOI":"10.1002\/cyto.b.22144"},{"key":"e_1_2_12_10_1","doi-asserted-by":"publisher","DOI":"10.1002\/cyto.a.24811"},{"key":"e_1_2_12_11_1","doi-asserted-by":"publisher","DOI":"10.1515\/med-2023-0745"},{"key":"e_1_2_12_12_1","doi-asserted-by":"publisher","DOI":"10.3390\/ijms25052847"},{"key":"e_1_2_12_13_1","doi-asserted-by":"publisher","DOI":"10.1097\/CCO.0000000000000607"},{"key":"e_1_2_12_14_1","doi-asserted-by":"publisher","DOI":"10.1038\/nmeth.2365"},{"key":"e_1_2_12_15_1","doi-asserted-by":"publisher","DOI":"10.18632\/oncotarget.12430"},{"key":"e_1_2_12_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2016.2590950"},{"key":"e_1_2_12_17_1","doi-asserted-by":"publisher","DOI":"10.1038\/ncomms14825"},{"key":"e_1_2_12_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ebiom.2018.10.042"},{"key":"e_1_2_12_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR.2018.8546177"},{"key":"e_1_2_12_20_1","first-page":"1010","article-title":"Application of machine learning for automatic MRD assessment in paediatric acute myeloid leukaemia","volume":"1012","author":"Licandro R","year":"2018","journal-title":"Cancer Cells"},{"key":"e_1_2_12_21_1","doi-asserted-by":"publisher","DOI":"10.1038\/s42003-019-0415-5"},{"key":"e_1_2_12_22_1","doi-asserted-by":"publisher","DOI":"10.1002\/cyto.a.23897"},{"key":"e_1_2_12_23_1","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics10050317"},{"key":"e_1_2_12_24_1","doi-asserted-by":"publisher","DOI":"10.3390\/cancers13040629"},{"key":"e_1_2_12_25_1","doi-asserted-by":"publisher","DOI":"10.1200\/JCO.20.02308"},{"key":"e_1_2_12_26_1","doi-asserted-by":"publisher","DOI":"10.3390\/cancers14040898"},{"key":"e_1_2_12_27_1","doi-asserted-by":"crossref","unstructured":"WeijlerL KowarschF ReiterM et al. FATE: Feature\u2010Agnostic Transformer\u2010based Encoder for learning generalized embedding spaces in flow cytometry data. 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