Minimizing human-induced variability in quantitative angiography for a robust and explainable AI-based occlusion prediction in flow diverter-treated aneurysms.

Abstract

Authors

• Parmita Mondal
  Biomedical Department, University at Buffalo, Buffalo, New York, USA.
• Mohammad Mahdi Shiraz Bhurwani
  Department of Biomedical Engineering, University at Buffalo, State University of New York, Buffalo, New York, USA.
• Swetadri Vasan Setlur Nagesh
  Canon Stroke and Vascular Research Center, University at Buffalo, Buffalo, NY, USA.
• Pui Man Rosalind Lai
  Neurosurgery, University at Buffalo, Buffalo, NY, USA.
• Jason M Davies
  Philip R. Lee Institute for Health Policy Studies, School of Medicine, University of California, San Francisco.
• Elad I Levy
  Canon Stroke and Vascular Research Center, University at Buffalo, Buffalo, New York, USA; Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York, USA; Department of Neurosurgery, University at Buffalo, Buffalo, New York, USA.
• Kunal Vakharia
  Neurosurgery and Brain Repair, University of South Florida College of Medicine, Tampa, Florida, USA.
• Michael Levitt
  Department of Neurological Surgery, University of Washington, Seattle, Washington, USA.
• Adnan H Siddiqui
  2Canon Stroke and Vascular Research Center, University at Buffalo, the State University of New York, Buffalo, New York.
• Ciprian N Ionita
  Department of Medical Physics, University at Buffalo, State University of New York, Buffalo, New York, USA.

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