Artificial Intelligence in the Management of Intracranial Aneurysms: Current Status and Future Perspectives.

Journal: AJNR. American journal of neuroradiology
Published Date:

Abstract

Intracranial aneurysms with subarachnoid hemorrhage lead to high morbidity and mortality. It is of critical importance to detect aneurysms, identify risk factors of rupture, and predict treatment response of aneurysms to guide clinical interventions. Artificial intelligence has received worldwide attention for its impressive performance in image-based tasks. Artificial intelligence serves as an adjunct to physicians in a series of clinical settings, which substantially improves diagnostic accuracy while reducing physicians' workload. Computer-assisted diagnosis systems of aneurysms based on MRA and CTA using deep learning have been evaluated, and excellent performances have been reported. Artificial intelligence has also been used in automated morphologic calculation, rupture risk stratification, and outcomes prediction with the implementation of machine learning methods, which have exhibited incremental value. This review summarizes current advances of artificial intelligence in the management of aneurysms, including detection and prediction. The challenges and future directions of clinical implementations of artificial intelligence are briefly discussed.

Authors

  • Z Shi
    From the Department of Medical Imaging (Z.S., B.H., Q.Q.N., G.M.L., L.J.Z.), Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, China.
  • B Hu
    Department of Environmental Health, Harvard T.H. Chan School of Public Health, United States; Liberty Mutual Research Institute for Safety, United States. Electronic address: boyihu@hsph.harvard.edu.
  • U J Schoepf
    Division of Cardiovascular Imaging (U.J.S., R.H.S., D.M.D.), Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina.
  • R H Savage
    Division of Cardiovascular Imaging (U.J.S., R.H.S., D.M.D.), Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina.
  • D M Dargis
    Division of Cardiovascular Imaging (U.J.S., R.H.S., D.M.D.), Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina.
  • C W Pan
    DeepWise AI Lab (C.W.P., X.L.L.), Beijing, China.
  • X L Li
    College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China.
  • Q Q Ni
    From the Department of Medical Imaging (Z.S., B.H., Q.Q.N., G.M.L., L.J.Z.), Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, China.
  • G M Lu
    From the Department of Medical Imaging (Z.S., B.H., Q.Q.N., G.M.L., L.J.Z.), Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, China.
  • L J Zhang
    From the Department of Medical Imaging (Z.S., B.H., Q.Q.N., G.M.L., L.J.Z.), Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, China kevinzhlj@163.com.