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

Author

Listed:
  • Zhao Shi

    (Jinling Hospital, Medical School of Nanjing University)

  • Chongchang Miao

    (Lianyungang First People’s Hospital)

  • U. Joseph Schoepf

    (Medical University of South Carolina)

  • Rock H. Savage

    (Medical University of South Carolina)

  • Danielle M. Dargis

    (Medical University of South Carolina)

  • Chengwei Pan

    (Peking University)

  • Xue Chai

    (Affiliated Nanjing Brain Hospital, Nanjing Medical University)

  • Xiu Li Li

    (DeepWise AI lab.)

  • Shuang Xia

    (Tianjin First Central Hospital)

  • Xin Zhang

    (Jinling Hospital, Medical School of Nanjing University)

  • Yan Gu

    (Lianyungang First People’s Hospital)

  • Yonggang Zhang

    (Lianyungang First People’s Hospital)

  • Bin Hu

    (Jinling Hospital, Medical School of Nanjing University)

  • Wenda Xu

    (Jinling Hospital, Medical School of Nanjing University)

  • Changsheng Zhou

    (Jinling Hospital, Medical School of Nanjing University)

  • Song Luo

    (Jinling Hospital, Medical School of Nanjing University)

  • Hao Wang

    (DeepWise AI lab.)

  • Li Mao

    (DeepWise AI lab.)

  • Kongming Liang

    (DeepWise AI lab.)

  • Lili Wen

    (Jinling Hospital, Medical School of Nanjing University)

  • Longjiang Zhou

    (Jinling Hospital, Medical School of Nanjing University)

  • Yizhou Yu

    (DeepWise AI lab.)

  • Guang Ming Lu

    (Jinling Hospital, Medical School of Nanjing University)

  • Long Jiang Zhang

    (Jinling Hospital, Medical School of Nanjing University
    Jinling Hospital, Sothern Medical University)

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.

Suggested Citation

  • Zhao Shi & Chongchang Miao & U. Joseph Schoepf & Rock H. Savage & Danielle M. Dargis & Chengwei Pan & Xue Chai & Xiu Li Li & Shuang Xia & Xin Zhang & Yan Gu & Yonggang Zhang & Bin Hu & Wenda Xu & Chan, 2020. "A clinically applicable deep-learning model for detecting intracranial aneurysm in computed tomography angiography images," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-19527-w
    DOI: 10.1038/s41467-020-19527-w
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