Machine Learning-Assisted Prediction of Persistent Incomplete Occlusion in Intracranial Aneurysms From Angiographic Parametric Imaging-Derived Features.

Journal: Academic radiology
Published Date: Jun 2, 2025

Abstract

RATIONALE AND OBJECTIVES: To develop machine-learning (ML) models incorporating angiographic parametric imaging (API)-derived parameters in predicting persistent incomplete occlusion of intracranial aneurysms (IAs) after flow diverter (FD) treatment.

Authors

  • Sheng-Qi Hu
    Department of Interventional Neuroradiology, Beijing Neurosurgical Institute, Capital Medical University, 100070, Beijing, PR China (S.Q.H., M.T., T.L., W.L., X.Y.); Department of Neurosurgery, Beijing TianTan Hospital, Capital Medical University, 100070, Beijing, PR China (S.Q.H., M.T., T.L., W.L., X.Y.).
  • Mirzat Turhon
    Department of Interventional Neuroradiology, Beijing Neurosurgical Institute, Capital Medical University, Beijing, 100070, People's Republic of China.
  • Ting Liu
    School of Public Health, Shanxi Medical University, Taiyuan 030000, China.
  • Wenqiang Li
    Department of Interventional Neuroradiology, Beijing Neurosurgical Institute and Beijing Tiantan Hospital, Capital Medical University, No.119 South Fourth Ring West Road, Fengtai District, Beijing, 100050, China.
  • Xinjian Yang
    Department of Interventional Neuroradiology, Beijing Neurosurgical Institute and Beijing Tiantan Hospital, Capital Medical University, No.119 South Fourth Ring West Road, Fengtai District, Beijing, 100050, China. yangxinjian@voiceoftiantan.org.

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