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Rupture risk assessment in cerebral arteriovenous malformations: an ensemble model using hemodynamic and morphological features.

Haoyu ZhuLian LiuShikai LiangChao MaYuzhou ChangLonghui ZhangXiguang FuYuqi SongJiarui ZhangYupeng ZhangChuhan Jiang
Published in: Journal of neurointerventional surgery (2024)
Quantitative hemodynamic and morphological features extracted from DSA data serve as potential indicators for assessing the rupture risk of AVM. The ensemble model effectively integrated multidimensional features, demonstrating favorable performance in predicting subsequent rupture of AVM.
Keyphrases
  • risk assessment
  • human health
  • convolutional neural network
  • subarachnoid hemorrhage
  • electronic health record
  • high resolution
  • heavy metals
  • machine learning
  • mass spectrometry
  • brain injury