A deep learning model with data integration of ultrasound contrast-enhanced micro-flow cines, B-mode images, and clinical parameters for diagnosing significant liver fibrosis in patients with chronic hepatitis B.

Abstract

Authors

• Zhong Liu
  Science and Technology on Information Systems Engineering Laboratory, College of Information System and Management, National University of Defense Technology, Changsha, Hunan, China.
• Wei Li
  Department of Nephrology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
• Ziqi Zhu
  National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University, Shenzhen, People's Republic of China.
• Huiying Wen
• Ming-de Li
  Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080, People's Republic of China.
• Chao Hou
  Department of Urology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China.
• Hui Shen
  College of Mechatronics and Automation, National University of Defense Technology, Changsha, China.
• Bin Huang
  Department of Clinical Laboratory, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
• Yudi Luo
  National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, 1066 Xueyuan Road, Shenzhen, 518055, People's Republic of China.
• Wei Wang
  State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macau 999078, China.
• Xin Chen
  University of Nottingham, Nottingham, United Kingdom.

Keywords

External Resources