• 2026-05-08
• O.M. Orogbemi, B.O. Oguntunde, O. Olopade, B.S. Aribisala
• Volume 2, Issue 1

Abstract

Base skull fractures are critical injuries often associated with severe neurological complications. The skull base is a complex anatomical platform at the bottom of the cranium that cradles the brain, provides structural support and protection, and serves as the entrance and exit for major vascular and neural structures. While computed tomography (CT) imaging remains the gold standard for skull fracture diagnosis, the intricate anatomy of the skull base makes fracture detection particularly challenging, frequently leading to delayed diagnosis or misinterpretation in emergency settings. This systematic literature review focuses on deep learning (DL) models for the extraction of base skull features from CT images to enable accurate detection of skull base fractures. The review critically examines the evolution from traditional feature-based algorithms to advanced deep learning approaches, including convolutional neural networks, object detection, semantic segmentation, and hybrid multi-stage frameworks. The main aspects, such as public and private datasets, preprocessing strategies, algorithmic architectures, performance metrics, and clinical relevance, are analyzed. The findings highlight that hybrid deep learning frameworks integrating detection, segmentation, and classification stages demonstrate superior performance for early and accurate skull base fracture identification, thereby supporting improved clinical decision-making and patient outcomes.

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