Deep learning algorithm applied to plain CT images to identify superior mesenteric artery abnormalities.

Journal: European journal of radiology
Published Date: Feb 23, 2024

Abstract

OBJECTIVES: Atypical presentations, lack of biomarkers, and low sensitivity of plain CT can delay the diagnosis of superior mesenteric artery (SMA) abnormalities, resulting in poor clinical outcomes. Our study aims to develop a deep learning (DL) model for detecting SMA abnormalities in plain CT and evaluate its performance in comparison with a clinical model and radiologist assessment.

Authors

  • Junhao Mei
    Department of Interventional and Vascular Surgery, The Affiliated Changzhou Second People's Hospital of Nanjing Medical University, Changzhou, China.
  • Hui Yan
    School of Computer Science and Engineering, Nanjing University of Science and Technology, 210094, China. Electronic address: yanhui@mail.njust.edu.cn.
  • Zheyu Tang
    Department of Interventional and Vascular Surgery, The Affiliated Changzhou Second People's Hospital of Nanjing Medical University, Changzhou, China.
  • Zeyu Piao
    Department of Interventional and Vascular Surgery, The Affiliated Changzhou Second People's Hospital of Nanjing Medical University, Changzhou, China.
  • Yuan Yuan
    Department of Geriatrics, Beijing Jishuitan Hospital, Capital Medical University, Beijing, China.
  • Yang Dou
    Department of Radiology, The Affiliated Changzhou Second People's Hospital of Nanjing Medical University, Changzhou, China.
  • Haobo Su
    Department of Interventional Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China.
  • Chunfeng Hu
    Department of Radiology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China.
  • Mingzhu Meng
    Department of Radiology, The Affiliated Changzhou No. 2 People's Hospital of Nanjing Medical University, Changzhou, China.
  • Zhongzhi Jia
    Department of Interventional and Vascular Surgery, The Affiliated Changzhou Second People's Hospital of Nanjing Medical University, Changzhou, China. Electronic address: jiazhongzhi.1998@163.com.

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