Performance of deep learning-based autodetection of arterial stenosis on head and neck CT angiography: an independent external validation study.

Journal: La Radiologia medica
PMID: 37464200

Abstract

PURPOSE: To externally validate the performance of automated stenosis detection on head and neck CT angiography (CTA) and investigate the impact factors using an independent bi-center dataset with digital subtraction angiography (DSA) as the ground truth.

Authors

  • Yongwei Yang
    Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Yuzhong District, No. 74 Linjiang Rd, Chongqing, 400010, China.
  • Xinyue Huan
    Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Yuzhong District, No. 74 Linjiang Rd, Chongqing, 400010, China.
  • Dajing Guo
    Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, No. 74 Linjiang Rd, Yuzhong District, Chongqing, 400010, China. guodaj@hospital.cqmu.edu.cn.
  • Xiaolin Wang
    Department of Urology, Nantong Tumor Hospital, Nantong, Jiangsu, China.
  • Shengwen Niu
    Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Yuzhong District, No. 74 Linjiang Rd, Chongqing, 400010, China.
  • Kunhua Li
    Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, No. 74 Linjiang Rd, Yuzhong District, Chongqing, 400010, China.

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