{Reference Type}: Journal Article
{Title}: Innovations in detecting skull fractures: A review of computer-aided techniques in CT imaging.
{Author}: Liew YM;Ooi JH;Azman RR;Ganesan D;Zakaria MI;Mohd Khairuddin AS;Tan LK;
{Journal}: Phys Med
{Volume}: 124
{Issue}: 0
{Year}: 2024 Aug 11
{Factor}: 3.119
{DOI}: 10.1016/j.ejmp.2024.103400
{Abstract}: <B>BACKGROUND: </B>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.<BR><B>METHODS: </B>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.<BR><B>CONCLUSIONS: </B>With CAD systems, we stand on the cusp of significantly improving TBI management, underscoring the need for continued innovation in this field.