Identification of Mild Traumatic Brain Injuries by Machine Learning using Whole-Brain Functional Activity

The accurate diagnosis of traumatic brain injury (TBI) has taken on a greater level of importance than it ever has before as a result of the challenges and complexities associated with the quantitative assessment of TBI as well as the growing significance of TBI in today's environment. Traumatic brain injuries are a prominent cause of death and disability in young individuals. The prevalence of mild traumatic brain injuries (mTBI) is rising. Around 1.6 million people worldwide acquire them annually. These injuries are brought on by an external force that impacts the head, such as blunt trauma, items that penetrate the skull, or blast waves. Using machine learning, we provide a comprehensive analysis of the functional activity across the whole brain in this research. To categorize all of the brain's activity, the concepts of machine learning are examined. As part of this review study, numerous connected literature studies will be carried out in order to gather and synthesize the methods and alternatives to identify mild traumatic brain injuries using machine learning and whole-brain functional activity.