更快的区域卷积神经网络（Faster R-CNN）在网织红细胞方法学比较与评估中的效用
Utility of Faster R-CNN in methodological comparison and evaluation of reticulocytes.
作者信息
Sun Shengli, Wang Geng, Zhang Binyao, Wang Fei, Wu Wei
机构信息
Department of Clinical Laboratory, Peking Union Medical College Hospital, Beijing, China.
出版信息
Front Physiol. 2024 May 31;15:1373103. doi: 10.3389/fphys.2024.1373103. eCollection 2024.
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.
目的
本研究旨在评估使用智能学习系统Faster R-CNN（一种利用深度神经网络开发的网织红细胞图像检测系统）对网织红细胞进行方法学比较的情况。
方法
我们选取了59份EDTA-K2抗凝全血样本，并在实验室中使用7台不同的Sysmex XN全自动血液分析仪结合Faster R-CNN计算RET%。我们对这些方法进行了比较和评估，并对各项检测结果之间的相关性进行了统计学分析。
结果
结果表明，7台Sysmex XN全自动血液分析仪与Faster R-CNN在检测RET%方面具有高度一致性。相关系数分别为0.987、0.984、0.986、0.987、0.987、0.988和0.986。
结论
我们发现，我们实验室中使用Faster R-CNN系统的Sysmex XN全自动血液分析仪符合网织红细胞检测方法学比较的要求，并且这种智能学习系统可以成为一种有用的临床工具。
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