Characterizing and revealing biomarkers on patients with Cerebral Amyloid Angiopathy using artificial intelligence

Dissertação de mestrado em BioinformáticaCerebral Amyloid Angiopathy is a cerebrovascular disorder resulting from the deposition of an amyloidogenic protein in small and medium sized cortical and leptomeningeal vessels. A primary cause of spontaneous intracerebral haemorrhages, it manifests predominantly in the elder population. Although CAA is a common neuropathological finding on itself, it is also known to frequently occur in conjunction with Alzheimer’s disease, being sometimes misdiagnosed. Currently, CAA diagnosis is generally conducted by post-mortem examination or, in live patients by the examination of an evacuated hematoma or brain biopsy samples, which are typically unavailable. Therefore, a reliable and non-invasive method for diagnosing CAA would facilitate the clinical decision making and accelerate the clinical intervention. The main goal of this dissertation is to study the application of Machine Learning (ML) to reveal possible biomarkers to aid the diagnosis and early medical intervention, and better understand the disease. Therefore, three scenarios were tested: Classification of four neurodegenerative diseases with annotation data obtained from visual rating scores, age and gender; Classification of the diseases with radiomic data derived from the patient’s MRI; and a combination of the previous experiments. The results show that the application of Artificial intelligence in the medical field brings advantages to support the physicians in the decision making process and, at some point, make a correct prediction of the disease label. Although the results are satisfactory, there are still improvements to be done. For instance, image segmentation of cerebral lesions or brain regions and additional clinical information of the patients would be of value.Angiopatia Amiloide Cerebral (AAC) é uma doença vascular cerebral resultante da deposição de matéria amiloide. Principal causa de hemorragias cerebral espontâneas, a AAC manifesta se predominantemente na população idosa. Embora a AAC seja uma doença que por si só tem um grande impacto no grupo etário referido, ocorre em simultâneo com inúmeras outras doenças neurodegenerativas, como a doença de Alzheimer. Atualmente, o diagnóstico de AAC realiza-se quer em post-mortem, quer em pacientes vivos. No entanto, o diagnóstico em vida é conseguido por meio de biópsias de tecidos cerebrais, sendo um método invasivo, o que dificulta a intervenção clínica. Deste modo, torna-se imperativa a procura de alternativas fiáveis e não invasivas em vida para auxiliar o diagnóstico da doença e permitir a melhoria da qualidade de vida do paciente. Perante os progressos na área da tecnologia e medicina, esta dissertação propõe o estudo da aplicação de algoritmos de Machine Learning (ML) para revelar possíveis biomarcadores para auxiliar o diagnóstico e permitir uma intervenção precoce. Deste modo, foram testados três cenários distintos: a classificação de quatro doenças neurodegenerativas com dados anotados obtidos a partir de métricas visuais de avaliação da atrofia, idade e sexo; a classificação das doenças com dados gerados a partir de métodos radiómicos; e uma combinação das duas abordagens anteriores. Neste documento apresenta-se e discute-se os resultados obtidos com a aplicação de quatro diferentes algoritmos de ML que visam a deteção automática da doença associada à imagem testada. Adicionalmente, é feita uma análise crítica de quais as características mais relevantes que levaram à tomada de decisão por parte do algoritmo. Os resultados demonstram que através de aplicação de metodologias automáticas é possível o auxílio ao diagnostico médico por especialistas e, no limite, a realização de diagnostico automático com elevada precisão. Finalmente, são apresentadas possíveis alternativas de trabalho futuro para que os resultados possam ser aperfeiçoados, como por exemplo, a segmentação das regiões de interesse, i.e., identificação das lesões, aquando da anotação por especialistas. Mediante a inclusão dessa segmentação, uma vez que será mais especifica, os resultados serão, por sua vez, aprimorados

BIANCA (Brain Intensity AbNormality Classification Algorithm): a new tool for automated segmentation of white matter hyperintensities

Reliable quantification of white matter hyperintensities of presumed vascular origin (WMHs) is increasingly needed, given the presence of these MRI findings in patients with several neurological and vascular disorders, as well as in elderly healthy subjects. We present BIANCA (Brain Intensity AbNormality Classification Algorithm), a fully automated, supervised method for WMH detection, based on the k-nearest neighbour (k-NN) algorithm. Relative to previous k-NN based segmentation methods, BIANCA offers different options for weighting the spatial information, local spatial intensity averaging, and different options for the choice of the number and location of the training points. BIANCA is multimodal and highly flexible so that the user can adapt the tool to their protocol and specific needs. We optimised and validated BIANCA on two datasets with different MRI protocols and patient populations (a “predominantly neurodegenerative” and a “predominantly vascular” cohort). BIANCA was first optimised on a subset of images for each dataset in terms of overlap and volumetric agreement with a manually segmented WMH mask. The correlation between the volumes extracted with BIANCA (using the optimised set of options), the volumes extracted from the manual masks and visual ratings showed that BIANCA is a valid alternative to manual segmentation. The optimised set of options was then applied to the whole cohorts and the resulting WMH volume estimates showed good correlations with visual ratings and with age. Finally, we performed a reproducibility test, to evaluate the robustness of BIANCA, and compared BIANCA performance against existing methods. Our findings suggest that BIANCA, which will be freely available as part of the FSL package, is a reliable method for automated WMH segmentation in large cross-sectional cohort studies. © 2016 The Author

Different patterns of white matter degeneration using multiple diffusion indices and volumetric data in mild cognitive impairment and Alzheimer patients

Alzheimeŕs disease (AD) represents the most prevalent neurodegenerative disorder that causes cognitive decline in old age. In its early stages, AD is associated with microstructural abnormalities in white matter (WM). In the current study, multiple indices of diffusion tensor imaging (DTI) and brain volumetric measurements were employed to comprehensively investigate the landscape of AD pathology. The sample comprised 58 individuals including cognitively normal subjects (controls), amnestic mild cognitive impairment (MCI) and AD patients. Relative to controls, both MCI and AD subjects showed widespread changes of anisotropic fraction (FA) in the corpus callosum, cingulate and uncinate fasciculus. Mean diffusivity and radial changes were also observed in AD patients in comparison with controls. After controlling for the gray matter atrophy the number of regions of significantly lower FA in AD patients relative to controls was decreased; nonetheless, unique areas of microstructural damage remained, e.g., the corpus callosum and uncinate fasciculus. Despite sample size limitations, the current results suggest that a combination of secondary and primary degeneration occurrs in MCI and AD, although the secondary degeneration appears to have a more critical role during the stages of disease involving dementia

Innovative MRI techniques in neuroimaging approaches for cerebrovascular diseases and vascular cognitive impairment

Cognitive impairment and dementia are recognized as major threats to public health. Many studies have shown the important role played by challenges to the cerebral vasculature and the neurovascular unit. To investigate the structural and functional characteristics of the brain, MRI has proven an invaluable tool for visualizing the internal organs of patients and analyzing the parameters related to neuronal activation and blood flow in vivo. Different strategies of imaging can be combined to obtain various parameters: (i) measures of cortical and subcortical structures (cortical thickness, subcortical structures volume); (ii) evaluation of microstructural characteristics of the white matter (fractional anisotropy, mean diffusivity); (iii) neuronal activation and synchronicity to identify functional networks across different regions (functional connectivity between specific regions, graph measures of specific nodes); and (iv) structure of the cerebral vasculature and its efficacy in irrorating the brain (main vessel diameter, cerebral perfusion). The high amount of data obtainable from multi-modal sources calls for methods of advanced analysis, like machine-learning algorithms that allow the discrimination of the most informative features, to comprehensively characterize the cerebrovascular network into specific and sensitive biomarkers. By using the same techniques of human imaging in pre-clinical research, we can also investigate the mechanisms underlying the pathophysiological alterations identified in patients by imaging, with the chance of looking for molecular mechanisms to recover the pathology or hamper its progression

A large margin algorithm for automated segmentation of white matter hyperintensity

Precise detection and quantification of white matter hyperintensity (WMH) is of great interest in studies of neurological and vascular disorders. In this work, we propose a novel method for automatic WMH segmentation with both supervised and semi-supervised large margin algorithms provided by the framework. The proposed algorithms optimize a kernel based max-margin objective function which aims to maximize the margin between inliers and outliers. We show that the semi-supervised learning problem can be formulated to learn a classifier and label assignment simultaneously, which can be solved efficiently by an iterative algorithm. The model is learned first via the supervised approach and then fine-tuned on a target image by using the semi-supervised algorithm. We evaluate our method on 88 brain fluid-attenuated inversion recovery (FLAIR) magnetic resonance (MR) images from subjects with vascular disease. Quantitative evaluation of the proposed approach shows that it outperforms other well known methods for WMH segmentation

A Review on the use of Artificial Intelligence Techniques in Brain MRI Analysis

Over the past 20 years, the global research going on in Artificial Intelligence in applica-tions in medication is a venue internationally, for medical trade and creating an ener-getic research community. The Artificial Intelligence in Medicine magazine has posted a massive amount. This paper gives an overview of the history of AI applications in brain MRI analysis to research its effect at the wider studies discipline and perceive de-manding situations for its destiny. Analysis of numerous articles to create a taxono-my of research subject matters and results was done. The article is classed which might be posted between 2000 and 2018 with this taxonomy. Analyzed articles have excessive citations. Efforts are useful in figuring out popular studies works in AI primarily based on mind MRI analysis throughout specific issues. The biomedical prognosis was ruled by way of knowledge engineering research in its first decade, whilst gadget mastering, and records mining prevailed thereafter. Together these two topics have contributed a lot to the latest medical domain