PT  - JOURNAL ARTICLE
AU  - Chao Sun
AU  - Ruijie Wang
AU  - Lanbo Zhao
AU  - Lu Han
AU  - Sijia Ma
AU  - Dongxin Liang
AU  - Lei Wang
AU  - Xiaoqian Tuo
AU  - Dexing Zhong
AU  - Qiling Li
TI  - Deep learning-based adaptive detection of fetal nucleated red blood cells
AID  - 10.1101/2020.03.06.980227
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.03.06.980227
4099  - http://biorxiv.org/content/early/2020/03/08/2020.03.06.980227.short
4100  - http://biorxiv.org/content/early/2020/03/08/2020.03.06.980227.full
AB  - Aim this study, we established an artificial intelligence system for rapid identification of fetal nucleated red blood cells (fNRBCs).Method Density gradient centrifugation and magnetic-activated cell sorting were used for the separation of fNRBCs from umbilical cord blood. The cell block technique was used for fixation. We proposed a novel preprocessing method based on imaging characteristics of fNRBCs for region of interest (ROI) extraction, which automatically segmented individual cells in peripheral blood cell smears. The discriminant information from ROIs was encoded into a feature vector and pathological diagnosis were provided by the prediction network.Results Four umbilical cord blood samples were collected and validated based on a large dataset containing 260 samples. Finally, the dataset was classified into 3,720 and 1,040 slides for training and testing, respectively. In the test set, classifier obtained 98.5% accuracy and 96.5% sensitivity.Conclusion Therefore, this study offers an effective and accurate method for fNRBCs preservation and identification.