Utility of Faster R-CNN in methodological comparison and evaluation of reticulocytes
In: Frontiers in Physiology, Jg. 15 (2024-05-01)
Online
academicJournal
Zugriff:
ObjectiveThe purpose of this study was to evaluate the methodological comparison of reticulocytes by using the intelligent learning system Faster R-CNN, a set of reticulocyte image detection systems developed using deep neural networks.MethodsWe selected 59 EDTA-K2 anticoagulated whole blood samples and calculated the RET% using seven different Sysmex XN full-automatic hematology analyzers with Faster R-CNN in the laboratory. We compared and evaluated the methods and statistically analyzed the correlation between the various test results.ResultsThe results indicated a high degree of consistency between the seven Sysmex XN full-automatic hematology analyzers and Faster R-CNN in detecting RET%. The correlation coefficients were 0.987, 0.984, 0.986, 0.987, 0.987, 0.988, and 0.986, respectively.ConclusionWe found that the Sysmex XN full-automatic hematology analyzers in our laboratory using the Faster R-CNN system met the requirements of the methodological comparison of reticulocyte detection and this intelligent learning system can be a useful clinical tool.
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Utility of Faster R-CNN in methodological comparison and evaluation of reticulocytes
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Autor/in / Beteiligte Person: | Sun, Shengli ; Wang, Geng ; Zhang, Binyao ; Wang, Fei ; Wu, Wei |
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Zeitschrift: | Frontiers in Physiology, Jg. 15 (2024-05-01) |
Veröffentlichung: | Frontiers Media S.A., 2024 |
Medientyp: | academicJournal |
ISSN: | 1664-042X (print) |
DOI: | 10.3389/fphys.2024.1373103 |
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