Advancing Intracranial Aneurysm Detection: A Comprehensive Systematic Review and Meta-analysis of Deep Learning Models Performance, Clinical Integration, and Future Directions.

Journal: Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
Published Date: Jun 1, 2025

Abstract

BACKGROUND: Cerebral aneurysms pose a significant risk to patient safety, particularly when ruptured, emphasizing the need for early detection and accurate prediction. Traditional diagnostic methods, reliant on clinician-based evaluations, face challenges in sensitivity and consistency, prompting the exploration of deep learning (DL) systems for improved performance.

Authors

  • Niloufar Delfan
    Faculty of Computer Engineering, Dept. of Artificial Intelligence Engineering, K. N. Toosi University of Technology, Tehran, Iran.
  • Fatemeh Abbasi
    Student Research Committee, Faculty of Medicine, Mazandaran University of Medical Sciences, Mazandaran, Iran.
  • Negar Emamzadeh
    Doctor of Medicine (MD), Iran University of Medical Sciences, Tehran, Iran.
  • Amirmohammad Bahri
    Student Research Committee, School of Medicine, Iran University of Medical Science, Tehran, Iran.
  • Mansour Parvaresh Rizi
    Department of Neurosurgery, Hazrat Rasool Hospital, Iran University of Medical Sciences, Tehran, Iran.
  • Alireza Motamedi
    Student Research Committee, School of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran.
  • Behzad Moshiri
    School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran; Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, Canada.
  • Arad Iranmehr
    Department of Neurosurgery, Tehran University of Medical Sciences, Imam Khomeini Hospital Complex (IKHC), Tehran, Iran.

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