Deep learning-based artificial intelligence model to assist thyroid nodule diagnosis and management: a multicentre diagnostic study.

Journal: The Lancet. Digital health
Published Date:

Abstract

BACKGROUND: Strategies for integrating artificial intelligence (AI) into thyroid nodule management require additional development and testing. We developed a deep-learning AI model (ThyNet) to differentiate between malignant tumours and benign thyroid nodules and aimed to investigate how ThyNet could help radiologists improve diagnostic performance and avoid unnecessary fine needle aspiration.

Authors

  • Sui Peng
    Center for Precision Medicine, Sun Yat-sen University, Guangzhou, 510080, China.
  • Yihao Liu
    Department of Emergency, The First Medical Center to Chinese People's Liberation Army General Hospital, Beijing, China.
  • Weiming Lv
    Department of Breast and Thyroid Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
  • Longzhong Liu
    Department of Ultrasound, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangzhou, China.
  • Qian Zhou
    Department of Computer Science, City University of Hong Kong, Hong Kong, Hong Kong SAR, China.
  • Hong Yang
    Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, China.
  • Jie Ren
    Digital Clinical Measures, Translational Medicine, Merck & Co., Inc., Rahway, NJ, United States.
  • Guangjian Liu
    Institute of Pediatrics, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.
  • Xiaodong Wang
    Cardiovascular Department, TEDA International Cardiovascular Hospital, Tianjin, China.
  • Xuehua Zhang
    Department of Ultrasound, the Guangzhou Army General Hospital, Guangzhou, China.
  • Qiang Du
    Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China.
  • Fangxing Nie
    Xiaobaishiji, Beijing, China.
  • Gao Huang
    Department of Automation, Tsinghua University, Beijing 100084, China. huang-g09@mails.tsinghua.edu.cn
  • Yuchen Guo
    Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing, China.
  • Jie Li
    Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence Application Technology Research Institute, Shenzhen Polytechnic University, Shenzhen, China.
  • Jinyu Liang
    Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
  • Hangtong Hu
    Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
  • Han Xiao
    Department of Radiation Oncology, Zhongshan Hospital, Fudan University, Shanghai, China.
  • Zelong Liu
    Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
  • Fenghua Lai
    Department of Endocrinology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
  • Qiuyi Zheng
    Department of Endocrinology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
  • Haibo Wang
    Institute of Cardiovascular Diseases, Shanghai Jiao Tong University School of Medicine, 197 Ruijin 2nd Road, Shanghai, 200025, PR China.
  • Yanbing Li
    Department of Endocrinology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
  • Erik K Alexander
    Thyroid Section, Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA. Electronic address: ekalexander@bwh.harvard.edu.
  • Wei Wang
    State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macau 999078, China.
  • Haipeng Xiao
    Department of Endocrinology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China. Electronic address: xiaohp@mail.sysu.edu.cn.