A highly accurate delta check method using deep learning for detection of sample mix-up in the clinical laboratory.

Journal: Clinical chemistry and laboratory medicine
Published Date: Dec 29, 2021

Abstract

OBJECTIVES: Delta check (DC) is widely used for detecting sample mix-up. Owing to the inadequate error detection and high false-positive rate, the implementation of DC in real-world settings is labor-intensive and rarely capable of absolute detection of sample mix-ups. The aim of the study was to develop a highly accurate DC method based on designed deep learning to detect sample mix-up.

Authors

  • Rui Zhou
    College of New Energy and Environment, Jilin University, Changchun 130021, China.
  • Yu-Fang Liang
    Department of Laboratory Medicine, Beijing Chao-yang Hospital, Capital Medical University, Beijing, P.R. China.
  • Hua-Li Cheng
    Department of Laboratory Medicine, Beijing Chao-yang Hospital, Capital Medical University, Beijing, P.R. China.
  • Wei Wang
    State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macau 999078, China.
  • Da-Wei Huang
    Key Laboratory of the Zoological Systematics and Evolution, Institute of Zoology and.
  • Zhe Wang
    Department of Pathology, The Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen 518033, China.
  • Xiang Feng
    Shanghai Engineering Research Center of Digital Education Equipment, East China Normal University, Shanghai 200062, China.
  • Ze-Wen Han
    Inner Mongolia Wesure Date Technology Co., Ltd, Inner Mongolia, P.R. China.
  • Biao Song
    Inner Mongolia Wesure Date Technology Co., Ltd, Inner Mongolia, P.R. China.
  • Andrea Padoan
    Department of Laboratory Medicine, University-Hospital of Padova, via Giustiniani 2, Padova 35128, Italy.
  • Mario Plebani
    Department of Laboratory Medicine, University-Hospital of Padova, Padova, Italy.
  • Qing-Tao Wang
    Department of Laboratory Medicine, Beijing Chao-yang Hospital, Capital Medical University, Beijing, P.R. China.

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