Automated echocardiographic diastolic function grading: A hybrid multi-task deep learning and machine learning approach.

Journal: International journal of cardiology
PMID: 39218252

Abstract

BACKGROUND: Assessing left ventricular diastolic function (LVDF) with echocardiography as per ASE guidelines is tedious and time-consuming. The study aims to develop a fully automatic approach of this procedure by a lightweight hybrid algorithm combining deep learning (DL) and machine learning (ML).

Authors

  • Qizhe Cai
    Department of Ultrasound, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China. Electronic address: echocaiqizhe@163.com.
  • Mingming Lin
    Department of Ultrasound, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China.
  • Miao Zhang
    gRED Computational Science, Genentech, Inc., South San Francisco, California.
  • Yunyun Qin
    Department of Ultrasound, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China.
  • Yuanlong Meng
    GE HealthCare, China.
  • Jiangtao Wang
    Henan Children's Hospital, No.33, Longhu Outer Ring Road, Zheng Dong New District, Zhengzhou, Henan, China.
  • Chenlei Leng
    Department of Ultrasound, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China.
  • Weiwei Zhu
    Department of Urology, Bayi Children's Hospital, Affiliated to The Seventh Medical Center of Chinese PLA General Hospital, Beijing, People's Republic of China.
  • Jie Li
    Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence Application Technology Research Institute, Shenzhen Polytechnic University, Shenzhen, China.
  • Junjie You
    Department of Ultrasound, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China.
  • Xiuzhang Lu
    Department of Ultrasound, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China. Electronic address: lxz_echo@163.com.

Keywords

External Resources

Popular Topics
Recent Journals