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dc.contributor.authorPortakal, Oytun
dc.contributor.authorTavil, Betül
dc.contributor.authorKuskonmaz, Baris
dc.contributor.authorAytac, Selin
dc.contributor.authorHascelik, Gulsen
dc.date.accessioned2020-10-21T07:27:04Z
dc.date.available2020-10-21T07:27:04Z
dc.date.issued2011
dc.identifier.issn1098-2825
dc.identifier.urihttp://hdl.handle.net/11655/22984
dc.identifier.urihttps://doi.org/10.1002/jcla.20433
dc.description.abstractThis study was aimed to evaluate the analytical performance of an automated image analysis system (a pilot model of Diff Master™ Octavia) for the preclassification of leucocytes in children with hematological disease. Manual microscopy performed by pediatric hematologists was used as the reference method. Five mature cell class and blasts were evaluated. Diff Master Octavia correctly preclassified 87.4% of all leucocytes with a high reproducibility. The overall accuracy was found to be 93.0%. Clinical sensitivity was 97.7% and specificity was 76.0%. The average time per slide for Diff Master™ Octavia was 2.3 min lower than that of manual method. Our results indicated that the Diff Master™ Octavia can detect and preclassify leucocytes accurately; therefore, it can be used as an efficient and fast method in pediatric hematology routine. J. Clin. Lab. Anal. 25:71–75, 2011. © 2011 Wiley‐Liss, Inc.tr_TR
dc.language.isoengtr_TR
dc.publisherWILEYtr_TR
dc.rightsinfo:eu-repo/semantics/openAccesstr_TR
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectAutomated image analysis systemtr_TR
dc.subjectPreclassification of leucocytestr_TR
dc.subjectArtificial neural networkstr_TR
dc.titleAn Automated Image Analysis System Can be Beneficial in Preclassification of Leucocytes in Children with Hematological Disease.tr_TR
dc.typeinfo:eu-repo/semantics/articletr_TR
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.relation.journalJournal of Clinical Laboratory Analysistr_TR
dc.contributor.departmentTıbbi Biyokimyatr_TR
dc.identifier.volume25tr_TR
dc.identifier.issue71tr_TR
dc.identifier.startpage75tr_TR
dc.indexingWoStr_TR
dc.fundingYoktr_TR


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