A Deep Learning Model for Automatically Quantifying the Anterior Segment in Ultrasound Biomicroscopy Images of Implantable Collamer Lens Candidates.

Journal: Ultrasound in medicine & biology
PMID: 38777640

Abstract

OBJECTIVE: This study aimed to develop and evaluate a deep learning-based model that could automatically measure anterior segment (AS) parameters on preoperative ultrasound biomicroscopy (UBM) images of implantable Collamer lens (ICL) surgery candidates.

Authors

  • Jian Zhu
  • Yulin Yan
    Department of Ophthalmology, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, China.
  • Weiyan Jiang
    Eye Center, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, China.
  • Shaowei Zhang
    Eye Center, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, China.
  • Xiaoguang Niu
    Eye Center, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, China.
  • Shanshan Wan
    Department of Ophthalmology, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, China.
  • Yuyu Cong
    Eye Center, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, China.
  • Xiao Hu
    Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, United States.
  • Biqin Zheng
    Wuhan EndoAngel Medical Technology Company, Wuhan, China.
  • Yanning Yang
    Department of Ophthalmology, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, 430060, Hubei Province, China. ophyyn@163.com.

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