Artificial intelligence performance in ultrasound-based lymph node diagnosis: a systematic review and meta-analysis.

Journal: BMC cancer
PMID: 39806293

Abstract

BACKGROUND AND OBJECTIVES: Accurate classification of lymphadenopathy is essential for determining the pathological nature of lymph nodes (LNs), which plays a crucial role in treatment selection. The biopsy method is invasive and carries the risk of sampling failure, while the utilization of non-invasive approaches such as ultrasound can minimize the probability of iatrogenic injury and infection. With the advancement of artificial intelligence (AI) and machine learning, the diagnostic efficiency of LNs is further enhanced. This study evaluates the performance of ultrasound-based AI applications in the classification of benign and malignant LNs.

Authors

  • Xinyang Han
    The Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China.
  • Jingguo Qu
    The Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China.
  • Man-Lik Chui
    The Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China.
  • Simon Takadiyi Gunda
    The Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China.
  • Ziman Chen
    Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong. chenzm27@mail3.sysu.edu.cn.
  • Jing Qin
    School of Nursing, The Hong Kong Polytechnic University, Hong Kong, China.
  • Ann Dorothy King
    Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong, China.
  • Winnie Chiu-Wing Chu
  • Jing Cai
    Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China.
  • Michael Tin-Cheung Ying
    The Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China. michael.ying@polyu.edu.hk.

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