A clinically applicable deep-learning model for detecting intracranial aneurysm in computed tomography angiography images.

Journal: Nature communications
Published Date: Nov 30, 2020

Abstract

Intracranial aneurysm is a common life-threatening disease. Computed tomography angiography is recommended as the standard diagnosis tool; yet, interpretation can be time-consuming and challenging. We present a specific deep-learning-based model trained on 1,177 digital subtraction angiography verified bone-removal computed tomography angiography cases. The model has good tolerance to image quality and is tested with different manufacturers. Simulated real-world studies are conducted in consecutive internal and external cohorts, in which it achieves an improved patient-level sensitivity and lesion-level sensitivity compared to that of radiologists and expert neurosurgeons. A specific cohort of suspected acute ischemic stroke is employed and it is found that 99.0% predicted-negative cases can be trusted with high confidence, leading to a potential reduction in human workload. A prospective study is warranted to determine whether the algorithm could improve patients' care in comparison to clinicians' assessment.

Authors

  • Zhao Shi
    Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, Jiangsu, China.
  • Chongchang Miao
    Department of Radiology, The First People's Hospital of Lianyungang, Lianyungang Clinical College of Nanjing Medical University, Lianyungang, China.
  • U Joseph Schoepf
    Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Tower, 25 Courtenay Dr, Charleston, SC 29425-2260 (S.S.M., D.M., M.v.A., C.N.D.C., R.R.B., C.T., A.V.S., A.M.F., B.E.J., L.P.G., U.J.S.); Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt, Germany (S.S.M., T.J.V.); Stanford University School of Medicine, Department of Radiology, Stanford, Calif (D.M.); Division of Cardiothoracic Imaging, Nuclear Medicine and Molecular Imaging, Department of Radiology and Imaging Sciences, Emory University, Atlanta, Ga (C.N.D.C.); Division of Cardiology, Department of Medicine, Medical University of South Carolina, Charleston, SC (R.R.B.); Department of Cardiology and Intensive Care Medicine, Heart Center Munich-Bogenhausen, Munich, Germany (C.T.); Department of Cardiology, Munich University Clinic, Ludwig-Maximilians-University, Munich, Germany (C.T.); Siemens Medical Solutions USA, Malvern, Pa (P.S.); and Department of Emergency Medicine, Medical University of South Carolina, Charleston, SC (A.J.M.).
  • Rock H Savage
    Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA.
  • Danielle M Dargis
    Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Heart & Vascular Center, Medical University of South Carolina, Ashley River Tower, 25 Courtenay Drive, Charleston, SC, 29425-2260, USA.
  • Chengwei Pan
    Computer Science Department, School of EECS, Peking University, Beijing, 100089, P.R. China.
  • Xue Chai
    Department of Radiology, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, Jiangsu, 210002, P.R. China.
  • Xiu Li Li
    Deepwise AI Lab, Beijing, 100089, China.
  • Shuang Xia
    Department of Radiology, Tianjin First Central Hospital, Tianjin, China.
  • Xin Zhang
    First Department of Infectious Diseases, The First Affiliated Hospital of China Medical University, Shenyang, China.
  • Yan Gu
    Department of Radiology, The First People's Hospital of Lianyungang, Lianyungang Clinical College of Nanjing Medical University, Lianyungang, China.
  • Yonggang Zhang
    Department of Radiology, The First People's Hospital of Lianyungang, Lianyungang Clinical College of Nanjing Medical University, Lianyungang, China.
  • Bin Hu
    Department of Thoracic Surgery Beijing Chao-Yang Hospital Affiliated Capital Medical University Beijing China.
  • Wenda Xu
    Mechanical Engineering department in Virginia Tech.
  • Changsheng Zhou
    Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, No.305, Zhongshan East Road, Nanjing, 210002, China.
  • Song Luo
    Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, 210002, P.R. China.
  • Hao Wang
    Department of Cardiology, Second Medical Center, Chinese PLA General Hospital, Beijing, China.
  • Li Mao
    Deepwise AI Lab, Deepwise Inc, No.8 Haidian avenue, Sinosteel International Plaza, Beijing, 100080, China.
  • Kongming Liang
    DeepWise AI lab., Beijing, 100089, P.R. China.
  • Lili Wen
    Department of Neurosurgery, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, 210002, P.R. China.
  • Longjiang Zhou
    Department of Neurosurgery, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, 210002, P.R. China.
  • Yizhou Yu
    Department of Computer Science, The University of Hong Kong, Pok Fu Lam, Hong Kong.
  • Guang Ming Lu
  • Long Jiang Zhang

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