Advances in artificial intelligence have cultivated a strong interest in developing and
validating the clinical utilities of computer-aided diagnostic models. Machine learning for …

While the number of people suffering from traumatic brain injury (TBI) has increased
considerably in recent years, the multiple deficits of these patients makes designing the …

The detection and classification of traumatic brain injury (TBI) by medical professionals can
vary due to subjectivity and differences in experience. Thus, a computational approach for …

In 2010, the National Institute of Neurological Disorders and Stroke (NINDS) created a set of
common data elements (CDEs) to help standardize the assessment and reporting of …

to identify opportunities for intervention. Many of the acute neuroimaging AI applications will
ultimately become integrated into the CT scanner, thereby minimizing delays and errors …

Progression of intracranial hemorrhage is a common, potentially devastating complication
after moderate/severe traumatic brain injury (TBI). Clinicians have few tools to predict which …

Background Traumatic brain injury (TBI) requires rapid management to avoid secondary
injury or death. This study evaluated if a simple schema for quickly interpreting CT head …

Introduction A non-contrast CT head scan (NCCTH) is the most common cross-sectional
imaging investigation requested in the emergency department. Advances in computer vision …

ABSTRACT BACKGROUND Traumatic brain injury (TBI) has become a major source of
disability worldwide, increasing the interest in algorithms that use Artificial Intelligence (AI) to …

Traumatic Brain Injury (TBI) poses a significant global public health challenge, contributing
to high morbidity and mortality rates and placing a substantial economic burden on …