https://scholars.lib.ntu.edu.tw/handle/123456789/627253| Title: | Clinically validated machine learning algorithm for detecting residual diseases with multicolor flow cytometry analysis in acute myeloid leukemia and myelodysplastic syndrome | Authors: | BOR-SHENG KO Wang, Yu-Fen Li, Jeng-Lin Li, Chi-Cheng Weng, Pei-Fang SZU-CHUN HSU HSIN-AN HOU Huang, Huai-Hsuan MING YAO Lin, Chien-Ting JIA-HAU LIU CHENG-HONG TSAI Huang, Tai-Chung SHANG-JU WU Huang, Shang-Yi WEN-CHIEN CHOU Tien, Hwei-Fang Lee, Chi-Chun Tang, Jih-Luh |
Keywords: | Acute myeloid leukemia; Artificial intelligence; Minimal residual disease; Multicolor flow cytometry; Myelodysplastic syndrome | Issue Date: | Nov-2018 | Publisher: | ELSEVIER | Journal Volume: | 37 | Start page/Pages: | 91 | Source: | EBioMedicine | Abstract: | 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 |
| Appears in Collections: | 醫學系 |
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