Summary: There is an urgent need for an automated decision support system for diagnosis and prognosis of traumatic brain injuries (TBI). TBI is one of the leading causes of death in the modern world, and substantially contributes to disability and impairment. The early detection of TBI and its proper management presents an unfilled need. We therefore aim to supplement clinicians' decisions by developing a decision support system for monitoring and integrating available information of a TBI patient for accurate and quantitative diagnosis and prognosis. This project is the main component of a long-term goal of building a system that creates personalized treatment plans. Specifically, we intend to automatically detect and accurately quantify two critical abnormalities including shift in the brain's middle structure (Aim 1) and intracranial hemorrhage (Aim 2) from computed tomography (CT) head scans. In Aim 1, we develop a model for delineating the spatial shift in brain structure and its predictive power. We employ anatomical landmarks to detect a 3D deformed surface of the brain midline after TBI. Such an approach allows us to quantify the shifted volume, a measurement that is not currently achievable. Additionally, it provides accurate and timely access to conventional midline shift in a 2D CT slice. In Aim 2, we build a model for delineating intracranial hemorrhage and its predictive power. We implement a 3D convolutional neural network model to detect hemorrhagic regions and quantify and localize their volume. Currently, these measurements are inaccurate and not readily available due to the cumbersome manual process; instead a lesion's thickness in a 2D CT slice is used to assess its severity. In both Aim 1 and 2, we automatically calculate conventional and proposed volumetric and locational measurements and compare them to suggest the best diagnostic metric for each abnormality. Finally, in Aim 3, we build an automated pipeline for TBI severity assessment and outcome prediction. To this end, manual CT scan reads will be integrated with patient-level information available from electronic health records to achieve accurate data-driven diagnosis and prognosis. We implement machine learning approaches to build models capable of predicting short and long-term clinical outcomes. Our prediction models will be developed independently of our image processing algorithms. Upon achievement of Aims 1 and 2, automatically calculated information from CT scans will be incorporated into machine learning models. The proposed research is significant, because it is expected to advance TBI care, specifically within the “golden hour" post-injury. Ultimately, such a system has the potential to reduce delayed and missed diagnosis, thereby reducing TBI morbidity and mortality. Additionally, by preventing permanent and/or secondary injuries, and minimizing the time of hospitalization and rehabilitation, our system will contribute to reducing the annual $76 billion burden of TBI care in the U.S. In addition to innovation in the proposed approaches and their quantitative outputs, we aggregate four existing datasets to incorporate heterogeneity in both phenotypes and therapies, so the resulted model will be generalizable and applicable to real clinical settings.

Project Narrative: Traumatic brain injury, frequently referred to as a silent epidemic, involves about 1.7 million people in the U.S, among whom 50,000 will die, while 152,000 will suffer from long-term disability and impairment. The proposed research will automatically diagnose and assess the severity of critical abnormalities from computed tomography head scan, and integrate all available sources of clinical data using data science methods to make a personalized data-driven prognosis. It is expected that this technology will provide caregivers with critical information for early and proper management of injuries, thus saving lives and improving the quality of life of survivors by reducing second/permanent injuries; importantly, this system would enable people living in outlying and deprived regions lacking skilled clinicians benefit from more accurate diagnostics, and as a result, a better care.