Performance of deep learning in the detection of intracranial aneurysm: A systematic review and meta-analysis.

Journal: European journal of radiology
Published Date: Jul 29, 2022

Abstract

PURPOSE: Early detection and diagnosis of intracranial aneurysms (IAs) are particularly critical. Deep learning models (DLMs) are now widely used in the diagnosis of various diseases. Different DLMs have been developed to detect IAs. However, the overall performance of various DLMs for detecting IAs has not been evaluated. We aimed at exploring the performance of DLMs in the detection of IAs and measuring the effect of DLMs in assisting clinicians.

Authors

  • Feng Gu
    Institute of Computing Technology, Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing, China.
  • Xiaoxiao Wu
    Department of Neurosurgery & Brain and Nerve Research Laboratory, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province 215006, China; Suzhou Medical College of Soochow University, Suzhou, Jiangsu Province 215002, China.
  • Wenxue Wu
    Department of Neurosurgery & Brain and Nerve Research Laboratory, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province 215006, China; Suzhou Medical College of Soochow University, Suzhou, Jiangsu Province 215002, China.
  • Zilan Wang
    Department of Neurosurgery & Brain and Nerve Research Laboratory, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province 215006, China.
  • Xingyu Yang
    School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia, United States of America.
  • Zhouqing Chen
    Department of Neurosurgery & Brain and Nerve Research Laboratory, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province 215006, China. Electronic address: zqchen6@163.com.
  • Zhong Wang
    Department of Intensive Care Unit, The First Hospital of China Medical University, Shenyang, Liaoning, China.
  • Gang Chen
    Department of Orthopedics, West China Hospital, Sichuan University, Chengdu, Sichuan, China.

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