Clinically validated machine learning algorithm for detecting residual diseases with multicolor flow cytometry analysis in acute myeloid leukemia and myelodysplastic syndrome

BOR-SHENG KOWang, Yu-FenYu-FenWangLi, Jeng-LinJeng-LinLiLi, Chi-ChengChi-ChengLiWeng, Pei-FangPei-FangWengSZU-CHUN HSUHSIN-AN HOUHUAI-HSUAN HUANGMING YAOLin, Chien-TingChien-TingLinJIA-HAU LIUCHENG-HONG TSAITAI-CHUNG HUANGSHANG-JU WUSHANG-YI HUANGWEN-CHIEN CHOUHWEI-FANG TIENLee, Chi-ChunChi-ChunLeeJIH-LUH TANG2023-01-162023-01-162018-112352-3964https://scholars.lib.ntu.edu.tw/handle/123456789/627253Multicolor flow cytometry (MFC) analysis is widely used to identify minimal residual disease (MRD) after treatment for acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS). However, current manual interpretation suffers from drawbacks of time consuming and interpreter idiosyncrasy. Artificial intelligence (AI), with the expertise in assisting repetitive or complex analysis, represents a potential solution for these drawbacks.enAcute myeloid leukemia; Artificial intelligence; Minimal residual disease; Multicolor flow cytometry; Myelodysplastic syndromeClinically validated machine learning algorithm for detecting residual diseases with multicolor flow cytometry analysis in acute myeloid leukemia and myelodysplastic syndromejournal article10.1016/j.ebiom.2018.10.042303610632-s2.0-85055099856WOS:000451691900025https://api.elsevier.com/content/abstract/scopus_id/85055099856