Diagnostic performance of deep learning-based vessel extraction and stenosis detection on coronary computed tomography angiography for coronary artery disease: a multi-reader multi-case study.

Journal: La Radiologia medica
PMID: 36800112

Abstract

BACKGROUND: Post-processing and interpretation of coronary CT angiography (CCTA) imaging are time-consuming and dependent on the reader's experience. An automated deep learning (DL)-based imaging reconstruction and diagnosis system was developed to improve diagnostic accuracy and efficiency.

Authors

  • Wenjie Yang
    Department of Radiology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China. lisa_ywj@163.com.
  • Chihua Chen
    Department of Radiology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Yanzhao Yang
    Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Lei Chen
    Department of Chemistry, Stony Brook University Stony Brook NY USA.
  • Changwei Yang
    Department of Radiology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China.
  • Lianggeng Gong
    Department of Radiology, the Second Affiliated Hospital of Nanchang University, Nanchang, China.
  • Jianing Wang
    Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN 37235, USA. Electronic address: jianing.wang@vanderbilt.edu.
  • Feng Shi
    Department of Research and Development, Shanghai United Imaging Intelligence, Co., Ltd. Shanghai, China.
  • Dijia Wu
  • Fuhua Yan
    Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No.197 Ruijin Er Road, Shanghai 200025, China. Electronic address: yfh11655@rjh.com.cn.

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