OBJECTIVE Machine learning (ML) has become an increasingly popular tool for use in
neurosurgical research. The number of publications and interest in the field have recently …

In this narrative review, we explore the evolving role of machine learning (ML) in the
diagnosis, prognosis, and clinical management of traumatic brain injury (TBI). The …

OBJECTIVE The overuse of head CT examinations has been much discussed, especially
those for minor traumatic brain injury (TBI). In the disruptive era, machine learning (ML) is …

Objective Traumatic brain injury (TBI) disproportionately affects low-and middle-income
countries (LMICs). In these settings, accurate patient prognostication is both difficult and …

BACKGROUND: Machine learning (ML) holds promise as a tool to guide clinical decision
making by predicting in-hospital mortality for patients with traumatic brain injury (TBI) …

OBJECTIVE Intracranial saccular aneurysms are vascular malformations responsible for
80% of nontraumatic brain hemorrhage. Recently, flow diverters have been used as a less …

BACKGROUND: Current traumatic brain injury (TBI) prognostic calculators are commonly
used to predict the mortality and Glasgow Outcome Scale, but these outcomes are most …

ML-based, multimodal approaches offer transformative potential for personalized treatment
planning and patient management. Future directions include integrating digital twins and …

Abstract Background and Aims Traumatic brain injury (TBI) is a widespread global health
issue with significant economic consequences. However, no existing model exists to predict …

Hospitals in low-and middle-income countries (LMICs) could benefit from decision support
technologies to reduce time to triage, diagnosis, and surgery for patients with traumatic brain …