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Challenging the status flow: how artificial intelligence is advancing diagnosis of myelodysplastic neoplasms.

作者信息

Duetz Carolien, Westers Theresia M, Van de Loosdrecht Arjan A

机构信息

Department of Hematology, Amsterdam UMC Amsterdam, location VUmc, The Netherlands; Cancer Center Amsterdam.

出版信息

Haematologica. 2023 Sep 1;108(9):2271-2272. doi: 10.3324/haematol.2023.282998.

Abstract
摘要

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本文引用的文献

1
Molecular International Prognostic Scoring System for Myelodysplastic Syndromes.
NEJM Evid. 2022 Jul;1(7):EVIDoa2200008. doi: 10.1056/EVIDoa2200008. Epub 2022 Jun 12.
2
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Haematologica. 2023 Sep 1;108(9):2435-2443. doi: 10.3324/haematol.2022.282370.
3
Management of patients with lower-risk myelodysplastic syndromes.
Blood Cancer J. 2022 Dec 14;12(12):166. doi: 10.1038/s41408-022-00765-8.
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Diabetic retinopathy screening in the emerging era of artificial intelligence.
Diabetologia. 2022 Sep;65(9):1415-1423. doi: 10.1007/s00125-022-05727-0. Epub 2022 May 31.
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Machine Learning of Bone Marrow Histopathology Identifies Genetic and Clinical Determinants in Patients with MDS.
Blood Cancer Discov. 2021 Mar 22;2(3):238-249. doi: 10.1158/2643-3230.BCD-20-0162. eCollection 2021 May.
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Computational flow cytometry as a diagnostic tool in suspected-myelodysplastic syndromes.
Cytometry A. 2021 Aug;99(8):814-824. doi: 10.1002/cyto.a.24360. Epub 2021 May 12.
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Artificial intelligence for clinical oncology.
Cancer Cell. 2021 Jul 12;39(7):916-927. doi: 10.1016/j.ccell.2021.04.002. Epub 2021 Apr 29.
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Computational analysis of flow cytometry data in hematological malignancies: future clinical practice?
Curr Opin Oncol. 2020 Mar;32(2):162-169. doi: 10.1097/CCO.0000000000000607.
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The practical implementation of artificial intelligence technologies in medicine.
Nat Med. 2019 Jan;25(1):30-36. doi: 10.1038/s41591-018-0307-0. Epub 2019 Jan 7.

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