Automated Decision Support System for Traumatic Injuries

With trauma being one of the leading causes of death in the U.S., automated decision support systems that can accurately detect traumatic injuries and predict their outcomes are crucial for preventing secondary injuries and guiding care management. My dissertation research incorporates machine learning and image processing techniques to extract knowledge from structured (e.g., electronic health records) and unstructured (e.g., computed tomography images) data to generate real-time, robust, quantitative trauma diagnosis and prognosis. This work addresses two challenges: 1) incorporating clinical domain knowledge into deep convolutional neural networks using classical image processing techniques and 2) using post-hoc explainers to align black box predictive machine learning models with clinical domain knowledge. Addressing these challenges is necessary for developing trustworthy clinical decision-support systems that can be generalized across the healthcare system. Motivated by this goal, we introduce an explainable and expert-guided machine learning framework to predict the outcome of traumatic brain injury. We also propose image processing approaches to automatically assess trauma from computed tomography scans. This research comprises four projects. In the first project, we propose an explainable hierarchical machine learning framework to predict the long-term functional outcome of traumatic brain injury using information available in electronic health records. This information includes demographic data, baseline features, radiology reports, laboratory values, injury severity scores, and medical history. To build such a framework, we peer inside the black-box machine learning models to explain their rationale for each predicted risk score. Accordingly, additional layers of statistical inference and human expert validation are added to the model, which ensures the predicted risk score’s trustworthiness. We demonstrate that imposing statistical and domain knowledge “checks and balances” not only does not adversely affect the performance of the machine learning classifier but also makes it more reliable. In the second project, we introduce a framework for detecting and assessing the severity of brain subdural hematomas. First, the hematoma is segmented using a combination of hand-crafted and deep learning features. Next, we calculate the volume of the injured region to quantitatively assess its severity. We show that the combination of classical image processing and deep learning can outperform deep-learning-only methods to achieve improved average performance and robustness. In the third project, we develop a framework to identify and assess liver trauma by calculating the percentage of the liver parenchyma disrupted by trauma. First, liver parenchyma and trauma masks are segmented by employing a deep learning backbone. Next, these segmented regions are refined with respect to the domain knowledge about the location and intensity distribution of liver trauma. This framework accurately estimated the severity of liver parenchyma trauma. In the final project, we propose a kidney segmentation method for patients with blunt abdominal trauma. This model incorporates machine learning and active contour modeling to generate kidney masks on abdominal CT images. The resultant of this component can provide a region of interest for screening kidney traumas in future studies. Together, the four projects discussed in this thesis contribute to diagnosis and prognosis of trauma across multiple body regions. They provide a quantitative assessment of traumas that is a more accurate measurement of the risk for adverse health outcomes as an alternative to current qualitative and sometimes subjective current clinical practice.PHDBioinformaticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/168065/1/negarf_1.pd

Deep learning: a turning point in acute neurology

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Contribution of CT-Scan Analysis by Artificial Intelligence to the Clinical Care of TBI Patients

The gold standard to diagnose intracerebral lesions after traumatic brain injury (TBI) is computed tomography (CT) scan, and due to its accessibility and improved quality of images, the global burden of CT scan for TBI patients is increasing. The recent developments of automated determination of traumatic brain lesions and medical-decision process using artificial intelligence (AI) represent opportunities to help clinicians in screening more patients, identifying the nature and volume of lesions and estimating the patient outcome. This short review will summarize what is ongoing with the use of AI and CT scan for patients with TBI

Longitudinal diffusion tensor imaging and neuropsychological correlates in traumatic brain injury patients

Traumatic brain injury (TBI) often involves focal cortical injury and white matter (WM) damage that can be measured shortly after injury. Additionally, slowly evolving WM change can be observed but there is a paucity of research on the duration and spatial pattern of long-term changes several years post-injury. The current study utilized diffusion tensor imaging to identify regional WM changes in 12 TBI patients and nine healthy controls at three time points over a four year period. Neuropsychological testing was also administered to each participant at each time point. Results indicate that TBI patients exhibit longitudinal changes to WM indexed by reductions in fractional anisotropy (FA) in the corpus callosum, as well as FA increases in bilateral regions of the superior longitudinal fasciculus (SLF) and portions of the optic radiation (OR). FA changes appear to be driven by changes in radial (not axial) diffusivity, suggesting that observed longitudinal FA changes may be related to changes in myelin rather than to axons. Neuropsychological correlations indicate that regional FA values in the corpus callosum and sagittal stratum (SS) correlate with performance on finger tapping and visuomotor speed tasks (respectively) in TBI patients, and that longitudinal increases in FA in the SS, SLF, and OR correlate with improved performance on the visuomotor speed (SS) task as well as a derived measure of cognitive control (SLF, OR). The results of this study showing progressive WM deterioration for several years post-injury contribute to a growing literature supporting the hypothesis that TBI should be viewed not as an isolated incident but as a prolonged disease state. The observations of long-term neurological and functional improvement provide evidence that some ameliorative change may be occurring concurrently with progressive degeneration

Development of a lesion localisation tool to improve outcome prediction in Traumatic Brain Injury patients

Tese de mestrado integrado, Engenharia Biomédica e Biofísica (Engenharia Clínica e Instrumentação Médica) Universidade de Lisboa, Faculdade de Ciências, 2022Traumatic brain injury (TBI) is a highly heterogeneous pathology that poses severe health and socioeconomic problems on a global scale. Neuroimaging research and development has advanced its clinical care in numerous ways, as injured brains are being imaged and studied in greater detail. The size and location of TBI lesions are often necessary to accurately determine a prognosis, which is key in defining a patient-specific rehabilitation program. This dissertation aims to investigate the impact of lesion characteristics, such as volume and location, on outcome prediction in TBI patients. Lesion localisation was achieved by comparing annotated TBI lesions to a brain atlas. Furthermore, other lesion characteristics were examined across different Magnetic Resonance Imaging (MRI) sequences and scanners, with results suggesting that the use of different scanners or MRI contrasts introduced biases in said lesion characteristics. Patient outcome was predicted using four generalised linear models. Besides clinical variables, these models included lesion volume, group and location as predictors. Model comparison indicated that lesion volume could be beneficial for outcome prediction, but may be dependent on both lesion group and location. Overall, this methodology showed potential in uncovering the effect that certain lesion groups and/or locations have on patient outcome after TBI

Effect of tranexamic acid on intracranial haemorrhage and infarction in patients with traumatic brain injury: a randomised trial.

Background: The CRASH-3 trial hypothesized that Tranexamic Acid (TXA) could reduce intracranial bleeding and the risk of head injury death in patients with traumatic brain injury (TBI). Because “head injury death” includes death from intracranial bleeding, to simplify the trial procedures, the investigators did not collect data on the extent of intracranial bleeding in all trial patients. Furthermore, TXA may increase the occurrence of stroke, and this outcome was recorded in the trial outcome form, but cerebral infarction as seen on imaging was not. Additional information on the hypothesized mechanism of action of TXA in TBI could help explain the CRASH-3 trial results. Research questions, aims and hypotheses: The CRASH-3 Intracranial Bleeding Mechanistic Study (IBMS) sought to investigate whether the mechanism of action of TXA in TBI could be assessed using routinely collected brain imaging. If so, the IBMS aimed to explore the potential effects of TXA on intracranial bleeding and infarction. Specifically, it was hypothesised that TXA could reduce intracranial bleeding and/or increase cerebral infarction. Methods: The IBMS was nested within the CRASH-3 trial: a prospective, double-blind, parallel-arm, randomised trial. Patients eligible for the CRASH-3 trial, with a Glasgow Coma Scale (GCS) score of ≤ 12 or intracranial bleeding on pre-randomisation CT were eligible. Outcomes were examined on routinely collected brain scans done pre- and/or post-randomisation. The primary outcome is the volume of intra-parenchymal bleeding in patients randomised within three hours of injury. Secondary outcomes include new and progressive bleeding, post-neurosurgical bleeding, infarction, and a composite “poor outcome”. The primary outcome was analysed using a linear mixed model, and dichotomous outcomes using relative risks or hazard ratios. Findings: The IBMS included 14% of the CRASH-3 trial patients (n=1767/12,737): 884 TXA, 883 placebo. Patients had a median baseline GCS of 7 (IQR 3–10). Only 46% of patients were scanned pre- and post-randomisation (n=812/1767) and 35% were scanned post- but not pre-randomisation (n=614/1767). A total of 21% of patients had evidence of neurosurgical haemorrhage evacuation on a post-randomisation scan. There was no evidence for a reduction in intra-parenchymal bleeding with TXA (1.09, 95% CI 0.81–1.45) or in intracranial bleeding in neurosurgical patients (0.79, 95% CI 0.57–1.11). There was no evidence for a reduction in the composite (RR=1.01, 95% CI 0.93–1.10) or increase in the hazard of infarction with TXA (HR=1.31, 95% CI 0.95–1.80). In patients scanned pre- and post-randomisation, there was no evidence that TXA reduces progressive bleeding (RR=0.92, 95% CI 0.74–1.13) and no clear evidence that TXA reduces new bleeding (RR=0.86, 95% CI 0.72–1.02). Conclusions: Routine imaging cannot provide reliable information on the effects of TXA in TBI. The associated methodological flaws mean that the treatment effect estimates are not valid and precise. 1) The large proportion of missing post-randomisation scans could depend on whether a patient received TXA. 2) The inclusion of a large proportion of severely injured patients may dilute effect estimates towards the null. 3) The receipt of TXA may affect whether patients undergo neurosurgery, and this complicates the assessment of the effects of TXA using scans done post-randomisation and post-neurosurgery. Implications for future research: If a research protocol mandated that scans were done at a set time-point post-randomisation, this would reduce the risk of bias from missing outcomes. If less severely injured patients were included, this would reduce the occurrence of neurosurgery and missing outcomes as a result of death

Contusion Progression in Traumatic Brain Injury

Cerebral contusions that result from brain trauma have the propensity to enlarge over the days following injury, including extension of the haemorrhage core and swelling of the surrounding peri-contusional brain. This secondary injury increases the likelihood of death and severe disabil- ity, and interventions to prevent or limit contusion progression may improve clinical outcomes. In preclinical studies a number of pathophysiological process have been identified that contribute to brain oedema including blood brain barrier (BBB) disruption, neuroinflammation, and cerebral metabolic dysfunction. This work has set out to measure and quantify contusion progression in TBI patients, characterise BBB permeability with computed tomography imaging, and identify key mediators of this process using intracerebral microdialysis. • Study I: A robust and effective stereological method for measuring the volume of brain lesions was validated and applied to a cohort of TBI patients to examine the temporal course of contusion progression. Intracranial pressure and brain lactate/pyruvate ratio were found to be associated with the magnitude of contusion expansion. • Study II: Dynamic contrast enhanced computed tomography (DCE-CT) was applied in pa- tients with brain contusions to quantify BBB permeability. The imaging showed profound reduction in cerebral blood flow associated with contusions but did not show evidence of contrast extravasation or BBB permeability. • Study III: Paired microdialysis catheters, one inserted in proximity to a contusion and an- other in non-injured brain, were used to characterise the peri-contusional inflammatory response with a multiplex assay of 42 cytokines, chemokines, and growth factors. Peri- contusional tissue was found to exhibit an early pro-inflammatory signature. • Study IV: A paired microdialysis study of pericontusional expression of matrix metalloproteinases (MMP). Specific increase in the expression of MMP-9 was identified in pericontusional brain. • Study V: Nitric oxide (NOx) concentrations were assayed in pericontusional and uninjured brain with paired microdialysis. NOx levels were lower in pericontusional brain and exhibited significant correlations with brain glucose, pyruvate, and lactate. • Study VI: Microdialysis data from 619 TBI patients with collated to explore the physiological correlates of deranged metabolism and elevated LPR. Cerebral glucose was found to a key determinant of LPR. The implications of these findings in the context of existing knowledge of BBB permeability, in- flammation, and cerebral metabolism in TBI are discussed. Future investigations to clarify the mechanisms highlighted, and potential therapeutic studies directed at reducing contusion pro- gression and brain oedema are suggested

Traumatic brain injury with particular reference to diffuse traumatic axonal injury subpopulations

Traumatic brain injury (TBI) remains an important cause of morbidity and mortality within society. TBI may result in both focal and diffuse brain injury. Diffuse traumatic axonal injury (TAI) is an important pathological substrate of TBI, and can be associated with a range of clinical states, ranging from concussion through to death, the clinical severity being associated with a number of factors related to the injury. A retrospective study was conducted using 406 cases with TBI, from the archive of the Academic Department of Pathology (Neuropathology) University of Edinburgh, during the period from1982 and 2005. This cohort was sequential and provided a unique description of the range of pathologies associated with fatal TBI within the Edinburgh catchment area. All the data was collected on a proforma and analysed to provide a description of the incidence in the injury patterns among the Edinburgh cohort. This cohort was then used to provide cases to try and critically assess the mechanisms of axonal injury in TBI. A study was undertaken to investigate TAI in an experimental model of non-impact head injury in a gyrencephalic mammalian model (piglet model) and in human autopsy materials using immunohistochemical analysis of a range of antibodies, and to define the distribution of axonal injury with flow and neurofilament markers in TAI. A further objective was to examine the expression of β-APP as an indicator of impaired axonal transport, three neurofilament markers targeting NF-160, NF-200, and the phosphorylated form of the neurofilament heavy chain (NFH), in different anatomical regions of piglet and human brains. The double immunofluorescence labelling method was then employed to investigate the hypothesis of co-localisation between β-APP and each one of the previous neurofilament markers. The animal studies showed significant differences in NF-160 between sham and injured 3-5 days old piglet cases (6 hour survival) and between 3-5 days sham and injured, when stained with SMI-34 antibody. In 4 weeks old piglet cases (6 hour survival), immunoreactivity of β-APP was significantly higher in injured than control. No other significant differences for any of the antibodies were noted, based on age, velocity, and survival time. Human results suggested that the brainstem had a higher level of β-APP and NF-160 than the corpus callosum and internal capsule. Co-localisation of β-APP with NFs was not a consistent feature of TAI in piglet and human brains, suggesting that markers of impaired axonal transport and neurofilament accumulation are sensitive to TAI, but may highlight different populations involved in the evolution of TAI

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

Ph.DDOCTOR OF PHILOSOPH