Innovations in detecting skull fractures: A review of computer-aided techniques in CT imaging

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.