https://scholars.lib.ntu.edu.tw/handle/123456789/627253
標題: | Clinically validated machine learning algorithm for detecting residual diseases with multicolor flow cytometry analysis in acute myeloid leukemia and myelodysplastic syndrome | 作者: | BOR-SHENG KO Wang, Yu-Fen Li, Jeng-Lin Li, Chi-Cheng Weng, Pei-Fang SZU-CHUN HSU HSIN-AN HOU HUAI-HSUAN HUANG MING YAO Lin, Chien-Ting JIA-HAU LIU CHENG-HONG TSAI TAI-CHUNG HUANG SHANG-JU WU SHANG-YI HUANG WEN-CHIEN CHOU HWEI-FANG TIEN Lee, Chi-Chun JIH-LUH TANG |
關鍵字: | Acute myeloid leukemia; Artificial intelligence; Minimal residual disease; Multicolor flow cytometry; Myelodysplastic syndrome | 公開日期: | 十一月-2018 | 出版社: | ELSEVIER | 卷: | 37 | 起(迄)頁: | 91 | 來源出版物: | EBioMedicine | 摘要: | Multicolor 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. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/627253 | ISSN: | 2352-3964 | DOI: | 10.1016/j.ebiom.2018.10.042 |
顯示於: | 醫學系 |
在 IR 系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。