Affiliations 

  • 1 Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia. Electronic address: liewym@um.edu.my
  • 2 Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia; Graduate School of Biomedical Engineering, UNSW Sydney, New South Wales, Australia
  • 3 Department of Biomedical Imaging, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia
  • 4 Division of Neurosurgery, Department of Surgery, Faculty of Medicine, Universiti Malaya, 50603 Kuala Lumpur, Malaysia
  • 5 Academic Unit Emergency Medicine, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia
  • 6 Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
  • 7 Department of Biomedical Imaging, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia. Electronic address: lktan@um.edu.my
Phys Med, 2024 Jul 11;124:103400.
PMID: 38996627 DOI: 10.1016/j.ejmp.2024.103400

Abstract

BACKGROUND/INTRODUCTION: Traumatic brain injury (TBI) remains a leading cause of disability and mortality, with skull fractures being a frequent and serious consequence. Accurate and rapid diagnosis of these fractures is crucial, yet current manual methods via cranial CT scans are time-consuming and prone to error.

METHODS: This review paper focuses on the evolution of computer-aided diagnosis (CAD) systems for detecting skull fractures in TBI patients. It critically assesses advancements from feature-based algorithms to modern machine learning and deep learning techniques. We examine current approaches to data acquisition, the use of public datasets, algorithmic strategies, and performance metrics RESULTS: The review highlights the potential of CAD systems to provide quick and reliable diagnostics, particularly outside regular clinical hours and in under-resourced settings. Our discussion encapsulates the challenges inherent in automated skull fracture assessment and suggests directions for future research to enhance diagnostic accuracy and patient care.

CONCLUSION: With CAD systems, we stand on the cusp of significantly improving TBI management, underscoring the need for continued innovation in this field.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.