A validation of an entropy-based artificial intelligence for ultrasound data in breast tumors.

Journal: BMC medical informatics and decision making
PMID: 38166852

Abstract

BACKGROUND: The application of artificial intelligence (AI) in the ultrasound (US) diagnosis of breast cancer (BCa) is increasingly prevalent. However, the impact of US-probe frequencies on the diagnostic efficacy of AI models has not been clearly established.

Authors

  • Zhibin Huang
    Jinan University, Guangzhou, Guangdong, 510632, China.
  • Keen Yang
    Jinan University, Guangzhou, Guangdong, 510632, China.
  • Hongtian Tian
    Ultrasound Department, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University), Shenzhen, Guangdong, 518020, China.
  • Huaiyu Wu
    Jinan University, Guangzhou, Guangdong, 510632, China.
  • Shuzhen Tang
    The Second Clinical Medical College, Jinan University, 518020, Shenzhen, China.
  • Chen Cui
    Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China; University of Chinese Academy of Sciences, No.19A Yuquan Road, Beijing, 100049, China.
  • Siyuan Shi
    Department of Ultrasound, The Second Clinical Medical Collegeļ¼ŒJinan University, Guangdong, China.
  • Yitao Jiang
    Illuminate, LLC, Shenzhen, Guangdong, China; Microport Prophecy, Shanghai, China.
  • Jing Chen
    Department of Vascular Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi 530021, P.R. China.
  • Jinfeng Xu
    Department of Ultrasound, The Second Clinical Medical Collegeļ¼ŒJinan University, Guangdong, China.
  • Fajin Dong
    Department of Ultrasound, The Second Clinical Medical College, Jinan University, Shenzhen, China.

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