8,830 research outputs found
Acute Stroke Multimodal Imaging: Present and Potential Applications toward Advancing Care.
In the past few decades, the field of acute ischemic stroke (AIS) has experienced significant advances in clinical practice. A core driver of this success has been the utilization of acute stroke imaging with an increasing focus on advanced methods including multimodal imaging. Such imaging techniques not only provide a richer understanding of AIS in vivo, but also, in doing so, provide better informed clinical assessments in management and treatment toward achieving best outcomes. As a result, advanced stroke imaging methods are now a mainstay of routine AIS practice that reflect best practice delivery of care. Furthermore, these imaging methods hold great potential to continue to advance the understanding of AIS and its care in the future. Copyright © 2017 by Thieme Medical Publishers, Inc
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Interpretable classification of Alzheimer's disease pathologies with a convolutional neural network pipeline.
Neuropathologists assess vast brain areas to identify diverse and subtly-differentiated morphologies. Standard semi-quantitative scoring approaches, however, are coarse-grained and lack precise neuroanatomic localization. We report a proof-of-concept deep learning pipeline that identifies specific neuropathologies-amyloid plaques and cerebral amyloid angiopathy-in immunohistochemically-stained archival slides. Using automated segmentation of stained objects and a cloud-based interface, we annotate > 70,000 plaque candidates from 43 whole slide images (WSIs) to train and evaluate convolutional neural networks. Networks achieve strong plaque classification on a 10-WSI hold-out set (0.993 and 0.743 areas under the receiver operating characteristic and precision recall curve, respectively). Prediction confidence maps visualize morphology distributions at high resolution. Resulting network-derived amyloid beta (Aβ)-burden scores correlate well with established semi-quantitative scores on a 30-WSI blinded hold-out. Finally, saliency mapping demonstrates that networks learn patterns agreeing with accepted pathologic features. This scalable means to augment a neuropathologist's ability suggests a route to neuropathologic deep phenotyping
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 clÃnica. 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 possÃveis 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 crÃtica de quais as caracterÃsticas mais relevantes
que levaram à tomada de decisão por parte do algoritmo. Os resultados demonstram que
através de aplicação de metodologias automáticas é possÃvel o auxÃlio ao diagnostico médico
por especialistas e, no limite, a realização de diagnostico automático com elevada precisão.
Finalmente, são apresentadas possÃveis 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
Deep Learning in Cardiology
The medical field is creating large amount of data that physicians are unable
to decipher and use efficiently. Moreover, rule-based expert systems are
inefficient in solving complicated medical tasks or for creating insights using
big data. Deep learning has emerged as a more accurate and effective technology
in a wide range of medical problems such as diagnosis, prediction and
intervention. Deep learning is a representation learning method that consists
of layers that transform the data non-linearly, thus, revealing hierarchical
relationships and structures. In this review we survey deep learning
application papers that use structured data, signal and imaging modalities from
cardiology. We discuss the advantages and limitations of applying deep learning
in cardiology that also apply in medicine in general, while proposing certain
directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table
Artificial Intelligence in Acute Ischemic Stroke Subtypes According to Toast Classification: A Comprehensive Narrative Review
The correct recognition of the etiology of ischemic stroke (IS) allows tempestive interventions in therapy with the aim of treating the cause and preventing a new cerebral ischemic event. Nevertheless, the identification of the cause is often challenging and is based on clinical features and data obtained by imaging techniques and other diagnostic exams. TOAST classification system describes the different etiologies of ischemic stroke and includes five subtypes: LAAS (large-artery atherosclerosis), CEI (cardio embolism), SVD (small vessel disease), ODE (stroke of other determined etiology), and UDE (stroke of undetermined etiology). AI models, providing computational methodologies for quantitative and objective evaluations, seem to increase the sensitivity of main IS causes, such as tomographic diagnosis of carotid stenosis, electrocardiographic recognition of atrial fibrillation, and identification of small vessel disease in magnetic resonance images. The aim of this review is to provide overall knowledge about the most effective AI models used in the differential diagnosis of ischemic stroke etiology according to the TOAST classification. According to our results, AI has proven to be a useful tool for identifying predictive factors capable of subtyping acute stroke patients in large heterogeneous populations and, in particular, clarifying the etiology of UDE IS especially detecting cardioembolic sources
Machine Learning in Medical Image Analysis
Machine learning is playing a pivotal role in medical image analysis. Many algorithms based on machine learning have been applied in medical imaging to solve classification, detection, and segmentation problems. Particularly, with the wide application of deep learning approaches, the performance of medical image analysis has been significantly improved. In this thesis, we investigate machine learning methods for two key challenges in medical image analysis: The first one is segmentation of medical images. The second one is learning with weak supervision in the context of medical imaging.
The first main contribution of the thesis is a series of novel approaches for image segmentation. First, we propose a framework based on multi-scale image patches and random forests to segment small vessel disease (SVD) lesions on computed tomography (CT) images. This framework is validated in terms of spatial similarity, estimated lesion volumes, visual score ratings and was compared with human experts. The results showed that the proposed framework performs as well as human experts. Second, we propose a generic convolutional neural network (CNN) architecture called the DRINet for medical image segmentation. The DRINet approach is robust in three different types of segmentation tasks, which are multi-class cerebrospinal fluid (CSF) segmentation on brain CT images, multi-organ segmentation on abdomen CT images, and multi-class tumour segmentation on brain magnetic resonance
(MR) images. Finally, we propose a CNN-based framework to segment acute ischemic lesions on diffusion weighted (DW)-MR images, where the lesions are highly variable in terms of position, shape, and size. Promising results were achieved on a large clinical dataset.
The second main contribution of the thesis is two novel strategies for learning with weak supervision. First, we propose a novel strategy called context restoration to make use of the images without annotations. The context restoration strategy is a proxy learning process based on the CNN, which extracts semantic features from images without using annotations. It was validated on classification, localization, and segmentation problems and was superior to existing strategies. Second, we propose a patch-based framework using multi-instance learning to distinguish normal and abnormal SVD on CT images, where there are only coarse-grained labels available. Our framework was observed to work better than classic methods and clinical practice.Open Acces
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
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