Deep learning for spirometry quality assurance with spirometric indices and curves.

Journal: Respiratory research
PMID:

Abstract

BACKGROUND: Spirometry quality assurance is a challenging task across levels of healthcare tiers, especially in primary care. Deep learning may serve as a support tool for enhancing spirometry quality. We aimed to develop a high accuracy and sensitive deep learning-based model aiming at assisting high-quality spirometry assurance.

Authors

  • Yimin Wang
    Department of Electrical Engineering and Computer Science , University of Michigan , 1301 Beal Avenue , Ann Arbor , Michigan 48109-2122 , United States.
  • Yicong Li
    Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, China.
  • Wenya Chen
    National Center for Respiratory Medicine, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
  • Changzheng Zhang
    From the School of Electronic Information and Communications, Huazhong University of Science and Technology, South 1st Building, Luoyu Road 1037, Wuhan 430074, China (J.Y., C.H., Y.X.); Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China (M.X., O.A., J.L., T.J., X. Long); Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China (M.X., O.A., J.L., T.J., X. Long); Department of Radiology, Xin Cai People's Hospital, Xin Cai, China (Changde Li); and Huawei Technologies, Shenzhen, China (D.T., X. Liu, C.Z., Cixing Li).
  • Lijuan Liang
    National Center for Respiratory Medicine, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
  • Ruibo Huang
    National Center for Respiratory Medicine, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
  • Jianling Liang
    National Center for Respiratory Medicine, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
  • Dandan Tu
    From the School of Electronic Information and Communications, Huazhong University of Science and Technology, South 1st Building, Luoyu Road 1037, Wuhan 430074, China (J.Y., C.H., Y.X.); Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China (M.X., O.A., J.L., T.J., X. Long); Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China (M.X., O.A., J.L., T.J., X. Long); Department of Radiology, Xin Cai People's Hospital, Xin Cai, China (Changde Li); and Huawei Technologies, Shenzhen, China (D.T., X. Liu, C.Z., Cixing Li).
  • Yi Gao
    Department of Laboratory Medicine, Shengjing Hospital of China Medical University, Shenyang, China.
  • Jinping Zheng
    National Center for Respiratory Medicine, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
  • Nanshan Zhong
    Guangzhou National Laboratory, Guangzhou, China.