Machine Learning Based Non-Enhanced CT Radiomics for the Identification of Orbital Cavernous Venous Malformations: An Innovative Tool.

Journal: The Journal of craniofacial surgery
Published Date: Jan 12, 2022

Abstract

PURPOSE: To evaluate the capability of non-enhanced computed tomography (CT) images for distinguishing between orbital cavernous venous malformations (OCVM) and non-OCVM, and to identify the optimal model from radiomics-based machine learning (ML) algorithms.

Authors

  • Qinghe Han
    Radiology Department, The Second Hospital of Jilin University, Changchun.
  • Lianze Du
    Radiology Department, The Second Hospital of Jilin University, Changchun.
  • Yan Mo
    Deepwise AI Lab, Beijing Deepwise& League of PHD Technology Co., Ltd, Haidian District, 21st Floor, China Sinosteel Plaza, NO. 8, Haidian Avenue, Beijing, 100080, People's Republic of China.
  • Chencui Huang
    Department of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co., Ltd., Beijing, China.
  • Qinghai Yuan
    Radiology Department, The Second Hospital of Jilin University, Changchun.

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