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ORIGINAL RESEARCH article

Front. Physiol.
Sec. Medical Physics and Imaging
Volume 15 - 2024 | doi: 10.3389/fphys.2024.1373103

Utility of Faster R-CNN in Methodological Comparison and Evaluation of Reticulocytes

Provisionally accepted
Shengli Sun Shengli Sun Geng Wang Geng Wang Binyao Zhang Binyao Zhang Fei Wang Fei Wang Wei Wu Wei Wu *
  • Peking Union Medical College Hospital (CAMS), Beijing, Beijing Municipality, China

The final, formatted version of the article will be published soon.

    Objective: The 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. Methods: We 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. Results: The 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. Conclusion: We 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.

    Keywords: Intelligent learning system, Methodological comparison, reticulocyte, Clinical laboratory, Hematology analyzers

    Received: 19 Jan 2024; Accepted: 13 May 2024.

    Copyright: © 2024 Sun, Wang, Zhang, Wang and Wu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Wei Wu, Peking Union Medical College Hospital (CAMS), Beijing, 100730, Beijing Municipality, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.