Exploratory study on the enhancement of O-RADS application effectiveness for novice ultrasonographers via deep learning.

Journal: Archives of gynecology and obstetrics
PMID: 39579245

Abstract

PURPOSE: The study aimed to create a deep convolutional neural network (DCNN) model based on ConvNeXt-Tiny to identify classic benign lesions (CBL) from other lesions (OL) within the Ovarian-Adnexal Reporting and Data System (O-RADS), enhancing the system's utility for novice ultrasonographers.

Authors

  • Tao Liu
    Institute of Urology and Nephrology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
  • Kuo Miao
    Department of Ultrasound, Fourth Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China.
  • Gaoqiang Tan
    The Department of Ultrasound Medicine, Harbin Medical University Fourth Affiliated Hospital, Harbin, Heilongjiang, China.
  • Hanqi Bu
    The Department of Ultrasound Medicine, Harbin Medical University Fourth Affiliated Hospital, Harbin, Heilongjiang, China.
  • Mingda Xu
    Department of Medical Ultrasound, The Fourth Affiliated Hospital of Harbin Medical University, No. 37 Yiyuan Street, Nangang District, Harbin, 150001, Heilongjiang, China.
  • Qiming Zhang
  • Qin Liu
    School of Public Health and Management, Research Center for Medicine and Social Development, Innovation Center for Social risk Governance in Health, Chongqing Medical University, Chongqing 400016, China.
  • Xiaoqiu Dong
    Department of Ultrasound, Fourth Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China. Electronic address: Dongxq0451@163.com.

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