Machine Learning Applications in Traumatic Brain Injury: A Spotlight on Mild TBI

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 healthcare systems worldwide. The diagnosis of TBI relies on clinical information along with Computed Tomography (CT) scans. Addressing the multifaceted challenges posed by TBI has seen the development of innovative, data-driven approaches, for this complex condition. Particularly noteworthy is the prevalence of mild TBI (mTBI), which constitutes the majority of TBI cases where conventional methods often fall short. As such, we review the state-of-the-art Machine Learning (ML) techniques applied to clinical information and CT scans in TBI, with a particular focus on mTBI. We categorize ML applications based on their data sources, and there is a spectrum of ML techniques used to date. Most of these techniques have primarily focused on diagnosis, with relatively few attempts at predicting the prognosis. This review may serve as a source of inspiration for future research studies aimed at improving the diagnosis of TBI using data-driven approaches and standard diagnostic data.Comment: The manuscript has 34 pages, 3 figures, and 4 table

Automatic Annotation, Classification and Retrieval of Traumatic Brain Injury CT Images

Ph.DDOCTOR OF PHILOSOPH

INCIDENCE, RISK FACTORS, AND PREDICTION OF GASTROINTESTINAL AND INTRACRANIAL BLEEDING IN A COHORT OF OLDER VETERANS PRESCRIBED ORAL ANTICOAGULANTS

ABSTRACT Incidence, Risk Factors, and Prediction of Gastrointestinal and Intracranial Bleeding in a Cohort of Older Veterans Prescribed Oral Anticoagulants by Angela L. Laurio Advisor: Marianne (Mimi) C. Fahs, PhD, MPH Objectives: The objectives of this dissertation were to: (1) describe and compare the incidence and odds of gastrointestinal and intracranial bleeding in veterans age 50 to 89 who were prescribed warfarin, direct oral anticoagulants, or no oral anticoagulants; (2) identify risk factors for gastrointestinal and intracranial bleeding among older veterans prescribed oral anticoagulants, and to calculate the relative risk of bleeding over time through time-to-event analysis; and (3) develop and compare models to predict risk of gastrointestinal and intracranial bleeding among veterans age 50 to 89 who were prescribed warfarin or direct oral anticoagulants using traditional and machine learning methods. These objectives were designed to assist healthcare organizations meet goals for the reduction of anticoagulant-related adverse drug events. Methods: The three studies in this dissertation were carried out using a retrospective cohort design and data from the Veterans Health Administration, American Community Survey, and National Center for Health Statistics. In Study 1, incidence and odds of gastrointestinal and intracranial bleeding were calculated using the full cohort of subjects and compared across three groups: those with no prescription for oral anticoagulants, those with a prescription for a direct oral anticoagulant, and those with a prescription for warfarin. In Study 2, time-to-event analysis for gastrointestinal or intracranial bleeding was conducted and independent risk factors identified for subjects with a prescription for an oral anticoagulant (warfarin or direct oral anticoagulant). In Study 3, predictive models were developed using traditional algorithms and machine learning tools to predict risk of gastrointestinal or intracranial bleeding for subjects with a prescription for oral anticoagulants. The primary independent variable for all three studies was the subject’s prescription category: no oral anticoagulant, warfarin, or direct oral anticoagulant. The primary dependent variable was a dichotomous variable indicating the presence of an ICD-9 or ICD-10 code for gastrointestinal or intracranial bleeding in the electronic health record. Diagnosis codes for bleeding were associated with any type of clinical encounter, including inpatient admissions, outpatient visits, or emergency room visits. Each subject’s index date was the date of the first outpatient clinical encounter from October 1, 2010 to September 30, 2011 for the first study, and the first OAC prescription date on or after October 1, 2010 for the second and third studies. The cohort was drawn from patients with at least two primary care visits between October 1, 2010 and September 30, 2011 at a VISN-2 facility and assigned a primary care provider. Subjects included in the study did not have a prescription for an oral anticoagulant in the six months prior to index date. Results: Study 1 found that veterans who were not prescribed oral anticoagulants experienced an average of 9 to 10 times the number of gastrointestinal bleeding events, and an average of 7 to 8 times the intracranial bleeding events as would be expected in the general population. Using either a no blackout or 5-day blackout period approach, this study found lower incidence rates per 100 person-years for gastrointestinal and intracranial bleeding among veterans prescribed oral anticoagulants than previous studies on both veterans and non-veterans. This study also found lower odds of bleeding among veterans prescribed an oral anticoagulant than previous studies; this was the case regardless of approach. Finally, an important finding of Study 1 was the significant difference in incidence between the no blackout period and the 5-day blackout period approaches. Study 2 found that as the time from oral anticoagulant prescription increased, the proportion of veterans age 50 and older who did not experience a gastrointestinal or intracranial bleeding event after being prescribed warfarin was similar to those prescribed direct oral anticoagulants. Findings were similar veterans with a diagnosis of atrial fibrillation. This finding was reversed in subjects age 75 or older, but all of these differences were small and not statistically significant. This study also found that prescriptions for antidepressants or statins were the strongest risk factors for gastrointestinal or intracranial bleeding for all subjects, while history of bleeding was the strongest risk factor for subjects with a diagnosis of atrial fibrillation and for subjects age 75 or older. Study 3 found that a logistic regression model using the traditional ORBIT algorithm performed the best out of 12 models developed to predict gastrointestinal or intracranial bleeding risk. A logistic regression model with a subset of five variables performed second best. Two machine learning algorithms also performed fairly well in predicting bleeding risk: a lasso model and a CART model. None of the models performed well in all five of the measures evaluated. Conclusions: The greatest gains in preventing adverse drug events associated with oral anticoagulants will likely be realized with increased sharing and use of electronic health data, and the ability to discover predictive models that perform well on a variety of evaluation measures. Findings of the three studies presented here were mixed in terms of being consistent with previous research, and additional research is necessary to understand these differences. Reducing the occurrence of gastrointestinal and intracranial bleeding following prescription of oral anticoagulants is an on-going challenge, and while the studies presented in this dissertation shed some light on older veterans and risk of gastrointestinal and intracranial bleeding, there are no easy answers to solving this complex clinical problem

Computer aided assessment of CT scans of traumatic brain injury patients

A thesis submitted in partial fulfilment for the degree of Doctor of PhilosophyOne of the serious public health problems is the Traumatic Brain Injury, also known as silent epidemic, affecting millions every year. Management of these patients essentially involves neuroimaging and noncontrast CT scans are the first choice amongst doctors. Significant anatomical changes identified on the neuroimages and volumetric assessment of haemorrhages and haematomas are of critical importance for assessing the patients’ condition for targeted therapeutic and/or surgical interventions. Manual demarcation and annotation by experts is still considered gold standard, however, the interpretation of neuroimages is fraught with inter-observer variability and is considered ’Achilles heel’ amongst radiologists. Errors and variability can be attributed to factors such as poor perception, inaccurate deduction, incomplete knowledge or the quality of the image and only a third of doctors confidently report the findings. The applicability of computer aided dianosis in segmenting the apposite regions and giving ’second opinion’ has been positively appraised to assist the radiologists, however, results of the approaches vary due to parameters of algorithms and manual intervention required from doctors and this presents a gap for automated segmentation and estimation of measurements of noncontrast brain CT scans. The Pattern Driven, Content Aware Active Contours (PDCAAC) Framework developed in this thesis provides robust and efficient segmentation of significant anatomical landmarks, estimations of their sizes and correlation to CT rating to assist the radiologists in establishing the diagnosis and prognosis more confidently. The integration of clinical profile of the patient into image segmentation algorithms has significantly improved their performance by highlighting characteristics of the region of interest. The modified active contour method in the PDCAAC framework achieves Jaccard Similarity Index (JI) of 0.87, which is a significant improvement over the existing methods of active contours achieving JI of 0.807 with Simple Linear Iterative Clustering and Distance Regularized Level Set Evolution. The Intraclass Correlation Coefficient of intracranial measurements is >0.97 compared with radiologists. Automatic seeding of the initial seed curve within the region of interest is incorporated into the method which is a novel approach and alleviates limitation of existing methods. The proposed PDCAAC framework can be construed as a contribution towards research to formulate correlations between image features and clinical variables encompassing normal development, ageing, pathological and traumatic cases propitious to improve management of such patients. Establishing prognosis usually entails survival but the focus can also be extended to functional outcomes, residual disability and quality of life issues

The Role of Transient Vibration of the Skull on Concussion

Concussion is a traumatic brain injury usually caused by a direct or indirect blow to the head that affects brain function. The maximum mechanical impedance of the brain tissue occurs at 450±50 Hz and may be affected by the skull resonant frequencies. After an impact to the head, vibration resonance of the skull damages the underlying cortex. The skull deforms and vibrates, like a bell for 3 to 5 milliseconds, bruising the cortex. Furthermore, the deceleration forces the frontal and temporal cortex against the skull, eliminating a layer of cerebrospinal fluid. When the skull vibrates, the force spreads directly to the cortex, with no layer of cerebrospinal fluid to reflect the wave or cushion its force. To date, there is few researches investigating the effect of transient vibration of the skull. Therefore, the overall goal of the proposed research is to gain better understanding of the role of transient vibration of the skull on concussion. This goal will be achieved by addressing three research objectives. First, a MRI skull and brain segmentation automatic technique is developed. Due to bones’ weak magnetic resonance signal, MRI scans struggle with differentiating bone tissue from other structures. One of the most important components for a successful segmentation is high-quality ground truth labels. Therefore, we introduce a deep learning framework for skull segmentation purpose where the ground truth labels are created from CT imaging using the standard tessellation language (STL). Furthermore, the brain region will be important for a future work, thus, we explore a new initialization concept of the convolutional neural network (CNN) by orthogonal moments to improve brain segmentation in MRI. Second, the creation of a novel 2D and 3D Automatic Method to Align the Facial Skeleton is introduced. An important aspect for further impact analysis is the ability to precisely simulate the same point of impact on multiple bone models. To perform this task, the skull must be precisely aligned in all anatomical planes. Therefore, we introduce a 2D/3D technique to align the facial skeleton that was initially developed for automatically calculating the craniofacial symmetry midline. In the 2D version, the entire concept of using cephalometric landmarks and manual image grid alignment to construct the training dataset was introduced. Then, this concept was extended to a 3D version where coronal and transverse planes are aligned using CNN approach. As the alignment in the sagittal plane is still undefined, a new alignment based on these techniques will be created to align the sagittal plane using Frankfort plane as a framework. Finally, the resonant frequencies of multiple skulls are assessed to determine how the skull resonant frequency vibrations propagate into the brain tissue. After applying material properties and mesh to the skull, modal analysis is performed to assess the skull natural frequencies. Finally, theories will be raised regarding the relation between the skull geometry, such as shape and thickness, and vibration with brain tissue injury, which may result in concussive injury

Brain Injury

The present two volume book "Brain Injury" is distinctive in its presentation and includes a wealth of updated information on many aspects in the field of brain injury. The Book is devoted to the pathogenesis of brain injury, concepts in cerebral blood flow and metabolism, investigative approaches and monitoring of brain injured, different protective mechanisms and recovery and management approach to these individuals, functional and endocrine aspects of brain injuries, approaches to rehabilitation of brain injured and preventive aspects of traumatic brain injuries. The collective contribution from experts in brain injury research area would be successfully conveyed to the readers and readers will find this book to be a valuable guide to further develop their understanding about brain injury