Artificial Intelligence Empowered Traumatic Brain Injury Diagnosis: A Comprehensive Survey of Methods and Applications

In recent years, traumatic brain injury (TBI) has emerged as a critical yet under-recognized global health concern, contributing to significant mortality and long-term neurological impairment across all demographics. Computed tomography (CT) remains the gold standard for prompt detection of intracranial injuries post-TBI, and artificial intelligence (AI) is also exploited for empowering CT based TBI diagnosis. This survey reviews AI-driven approaches for TBI detection and prognosis on CT scans, highlights limitations obstructing their adoption in clinical workflows. Meanwhile, it surveys the publicly available datasets in this domain, encompassing aspects such as image resolution, the diversity of lesion types, and the application of state of the art (SOTA) approaches. Finally, we provide targeted recommendations to enhance secondary-injury modeling, expand dataset availability, and address prevalent challenges in primary injury assessment.