Recommendations for using artificial intelligence in clinical flow cytometry.
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| Title: | Recommendations for using artificial intelligence in clinical flow cytometry. |
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| Authors: | Ng DP; Department of Pathology, University of Utah, Salt Lake City, Utah, USA., Simonson PD; Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, New York, USA., Tarnok A; Department of Preclinical Development and Validation, Fraunhofer Institute for Cell Therapy and Immunology, IZI, Leipzig, Germany., Lucas F; Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, USA., Kern W; MLL Munich Leukemia Laboratory GmbH, Munich, Germany., Rolf N; BC Children's Hospital Research Institute, University of British Columbia, Vancouver, British Columbia, Canada., Bogdanoski G; Clinical Development & Operations Quality, R&D Quality, Bristol Myers Squibb, Princeton, New Jersey, USA., Green C; Translational Science, Ozette Technologies, Seattle, Washington, USA., Brinkman RR; Dotmatics, Inc, Boston, Massachusetts, USA., Czechowska K; Metafora Biosystems, PARIS, France. |
| Source: | Cytometry. Part B, Clinical cytometry [Cytometry B Clin Cytom] 2024 Jul; Vol. 106 (4), pp. 228-238. Date of Electronic Publication: 2024 Feb 26. |
| Publication Type: | Journal Article; Review |
| Journal Info: | Publisher: Wiley-Liss Country of Publication: United States NLM ID: 101235690 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1552-4957 (Electronic) Linking ISSN: 15524949 NLM ISO Abbreviation: Cytometry B Clin Cytom Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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| Header | DbId: mdl DbLabel: MEDLINE Ultimate An: 38407537 AccessLevel: 2 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Recommendations for using artificial intelligence in clinical flow cytometry. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AU" term="%22Ng+DP%22">Ng DP</searchLink>; Department of Pathology, University of Utah, Salt Lake City, Utah, USA.<br /><searchLink fieldCode="AU" term="%22Simonson+PD%22">Simonson PD</searchLink>; Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, New York, USA.<br /><searchLink fieldCode="AU" term="%22Tarnok+A%22">Tarnok A</searchLink>; Department of Preclinical Development and Validation, Fraunhofer Institute for Cell Therapy and Immunology, IZI, Leipzig, Germany.<br /><searchLink fieldCode="AU" term="%22Lucas+F%22">Lucas F</searchLink>; Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, USA.<br /><searchLink fieldCode="AU" term="%22Kern+W%22">Kern W</searchLink>; MLL Munich Leukemia Laboratory GmbH, Munich, Germany.<br /><searchLink fieldCode="AU" term="%22Rolf+N%22">Rolf N</searchLink>; BC Children's Hospital Research Institute, University of British Columbia, Vancouver, British Columbia, Canada.<br /><searchLink fieldCode="AU" term="%22Bogdanoski+G%22">Bogdanoski G</searchLink>; Clinical Development & Operations Quality, R&D Quality, Bristol Myers Squibb, Princeton, New Jersey, USA.<br /><searchLink fieldCode="AU" term="%22Green+C%22">Green C</searchLink>; Translational Science, Ozette Technologies, Seattle, Washington, USA.<br /><searchLink fieldCode="AU" term="%22Brinkman+RR%22">Brinkman RR</searchLink>; Dotmatics, Inc, Boston, Massachusetts, USA.<br /><searchLink fieldCode="AU" term="%22Czechowska+K%22">Czechowska K</searchLink>; Metafora Biosystems, PARIS, France. – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22101235690%22">Cytometry. Part B, Clinical cytometry</searchLink> [Cytometry B Clin Cytom] 2024 Jul; Vol. 106 (4), pp. 228-238. <i>Date of Electronic Publication: </i>2024 Feb 26. – Name: TypePub Label: Publication Type Group: TypPub Data: Journal Article; Review – Name: TitleSource Label: Journal Info Group: Src Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Wiley-Liss%22">Wiley-Liss </searchLink><i>Country of Publication: </i>United States <i>NLM ID: </i>101235690 <i>Publication Model: </i>Print-Electronic <i>Cited Medium: </i>Internet <i>ISSN: </i>1552-4957 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2215524949%22">15524949 </searchLink><i>NLM ISO Abbreviation: </i>Cytometry B Clin Cytom <i>Subsets: </i>MEDLINE |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=mdl&AN=38407537 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1002/cyto.b.22166 Languages: – Code: eng Text: English PhysicalDescription: Pagination: StartPage: 228 Titles: – TitleFull: Recommendations for using artificial intelligence in clinical flow cytometry. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Ng DP – PersonEntity: Name: NameFull: Simonson PD – PersonEntity: Name: NameFull: Tarnok A – PersonEntity: Name: NameFull: Lucas F – PersonEntity: Name: NameFull: Kern W – PersonEntity: Name: NameFull: Rolf N – PersonEntity: Name: NameFull: Bogdanoski G – PersonEntity: Name: NameFull: Green C – PersonEntity: Name: NameFull: Brinkman RR – PersonEntity: Name: NameFull: Czechowska K IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: 2024 Jul Type: published Y: 2024 Identifiers: – Type: issn-electronic Value: 1552-4957 Numbering: – Type: volume Value: 106 – Type: issue Value: 4 Titles: – TitleFull: Cytometry. Part B, Clinical cytometry Type: main |
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