Development of a deep-learning algorithm for etiological classification of subarachnoid hemorrhage using non-contrast CT scans.

Journal: European radiology
Published Date: May 17, 2025

Abstract

OBJECTIVES: This study aims to develop a deep learning algorithm for differentiating aneurysmal subarachnoid hemorrhage (aSAH) from non-aneurysmal subarachnoid hemorrhage (naSAH) using non-contrast computed tomography (NCCT) scans.

Authors

  • Lingxu Chen
    Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
  • Xiaochen Wang
    Pennsylvania State University, State College, PA, USA.
  • Yuanjun Li
    Department of Radiology, Zhongshan Hospital Affiliated to Xiamen University, Hubinnan Road, Xiamen, China.
  • Yang Bao
    Neusoft Medical Systems, Shenyang, China.
  • Sihui Wang
    Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
  • Xuening Zhao
    Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
  • Mengyuan Yuan
    Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
  • Jianghe Kang
    Department of Radiology, Zhongshan Hospital Affiliated to Xiamen University, Hubinnan Road, Xiamen, China.
  • Shengjun Sun
    Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.

Keywords

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