Diagnostic performance of neural network algorithms in skull fracture detection on CT scans: a systematic review and meta-analysis.

Journal: Emergency radiology
Published Date: Dec 16, 2024

Abstract

BACKGROUND AND AIM: The potential intricacy of skull fractures as well as the complexity of underlying anatomy poses diagnostic hurdles for radiologists evaluating computed tomography (CT) scans. The necessity for automated diagnostic tools has been brought to light by the shortage of radiologists and the growing demand for rapid and accurate fracture diagnosis. Convolutional Neural Networks (CNNs) are a potential new class of medical imaging technologies that use deep learning (DL) to improve diagnosis accuracy. The objective of this systematic review and meta-analysis is to assess how well CNN models diagnose skull fractures on CT images.

Authors

  • Guive Sharifi
    Skull base Research Center, Loghman Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • Ramtin Hajibeygi
    Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Tehran University of Medical Science, Tehran, Iran; Tehran University of Medical Science (TUMS), School of Medicine, Tehran, Iran.
  • Seyed Ali Modares Zamani
    Department of Neurosurgery, Iranian Hospital, Dubai, United Arab Emirates.
  • Ahmed Mohamedbaqer Easa
    Department of Radiology Technology, Collage of Health and Medical Technology, Al-Ayen Iraqi University, Thi-Qar, 64001, Iraq.
  • Ashkan Bahrami
    Student Research Committee, Kashan University of Medical Science, Kashan, Iran.
  • Reza Eshraghi
    Student Research Committee, Kashan University of Medical Sciences, Kashan, Iran.
  • Maral Moafi
    Cell Biology and Anatomical Sciences, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • Mohammad Javad Ebrahimi
    Cell Biology and Anatomical Sciences, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • Mobina Fathi
    School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • Arshia Mirjafari
    Department of Radiological Sciences, University of California, Los Angeles, CA, USA.
  • Janine S Chan
    Keck School of Medicine of USC, Los Angeles, CA, USA.
  • Irene Dixe de Oliveira Santo
    Department of Radiology and Biomedical Imaging, Yale School of Medicine, CT, USA.
  • Mahsa Asadi Anar
    College of Medicine, University of Arizona, Tucson, AZ, USA.
  • Omidvar Rezaei
    Skull base Research Center, Loghman Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • Long H Tu
    Department of Radiology and Biomedical Imaging, Yale School of Medicine, CT, USA. long.tu@yale.edu.

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