From Imaging to Outcomes: Machine Learning and AI for TBI Detection

Background Traumatic brain injury (TBI), remain a major public health problem, often leading to severe and lasting consequences. Early and accurate diagnosis is essential for optimal treatment and patient prognosis. However, there are still issues with conventional diagnostic methods related to timeliness, sensitivity and specificity. This paper explores how Machine Learning (ML) and Artificial Intelligence (AI) can aid in improving city level diagnostics of Traumatic Brain Injury (TBI), within a similar arena as that of medical imaging or patient outcome prediction. This article reviews novel applications of artificial intelligence and machine learning with Traumatic Brain Injury (TBI) and specifically focuses on the use of advanced imaging modalities CT, MRI as well as computer-aided diagnostic systems, decision support tools using AI algorithms, predictive modeling for patient prognosis. In our study, we de-constructed various machine learning techniques and analysed the pros, cons and potential medical usage cases. Also treated are the challenges of data availability, algorithmic malpractices, and ethical questions posed by this technology being deployed. Furthermore, it offers very interesting perspectives of future courses that should be taken to make an optimal use out of artificial intelligence and machine learning for the diagnosis and management, with emphasis on the input welcome from all professions. This study is aimed at addressing the current state of research across the board in TBI, serving as a tool for policymakers, physicians, and researchers working on Traumatic Brain Injury.