Intelligent diagnosis of thyroid nodules with AI ultrasound assistance and cytology classification.

Journal: Frontiers in endocrinology
Published Date:

Abstract

OBJECTIVE: Accurate evaluation of thyroid nodules is crucial for effective management; however, methods such as ultrasonography and Fine Needle Aspiration Cytology (FNAC) can be subjective and operator-dependent. Indeterminate thyroid nodules (ITNs) complicate diagnosis, coming at the expense of time, money, and potentially additional FNA samplings, causing more discomfort for the patients. Recent advancements in artificial intelligence (AI) assisted ultrasound diagnosis system have demonstrated excellent diagnostic performance and the potential to aid in the differentiation of ITNs. This study aims to develop an AI classifier that integrates the AI-assisted ultrasound diagnosis system, FNAC, and demographic data to enhance the differentiation of benign and malignant thyroid nodules, and to compare the diagnostic performance of the models, with a focus on diagnosing ITNs.

Authors

  • Xiaojuan Cai
    Department of Breast and Thyroid Surgery, The Second Affiliated Hospital of Soochow University, Suzhou, China.
  • Ya Zhou
    School of Marxism, Southeast University, Nanjing, Jiangsu 211189, China.
  • Jie Ren
    Digital Clinical Measures, Translational Medicine, Merck & Co., Inc., Rahway, NJ, United States.
  • Jinrong Wei
    Department of Breast and Thyroid Surgery, The Second Affiliated Hospital of Soochow University, Suzhou, China.
  • Shiyu Lu
    School of Computer Science and Technology, Changchun University of Science and Technology, Changchun, Jilin, China.
  • Hanbing Gu
    Department of Breast and Thyroid Surgery, The Second Affiliated Hospital of Soochow University, Suzhou, China.
  • Weizhe Xu
    Biomedical and Health Informatics, University of Washington.
  • Xun Zhu
    Molecular Biosciences and Bioengineering Graduate Program, University of Hawaii at Manoa, Honolulu, HI, United States of America.