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Cerebral Hemorrhage

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Association of baseline hematoma and edema volumes with one-year outcome and long-term survival after spontaneous intracerebral hemorrhage: A community-based inception cohort study.

International journal of stroke : official journal of the International Stroke Society
BACKGROUND: Hospital-based studies have reported variable associations between outcome after spontaneous intracerebral hemorrhage and peri-hematomal edema volume.

Artificial neural network based prediction of postthrombolysis intracerebral hemorrhage and death.

Scientific reports
Despite the salient benefits of the intravenous tissue plasminogen activator (tPA), symptomatic intracerebral hemorrhage (sICH) remains a frequent complication and constitutes a major concern when treating acute ischemic stroke (AIS). This study expl...

Automated detection of cerebral microbleeds in MR images: A two-stage deep learning approach.

NeuroImage. Clinical
Cerebral Microbleeds (CMBs) are small chronic brain hemorrhages, which have been considered as diagnostic indicators for different cerebrovascular diseases including stroke, dysfunction, dementia, and cognitive impairment. However, automated detectio...

3D Deep Neural Network Segmentation of Intracerebral Hemorrhage: Development and Validation for Clinical Trials.

Neuroinformatics
Intracranial hemorrhage (ICH) occurs when a blood vessel ruptures in the brain. This leads to significant morbidity and mortality, the likelihood of which is predicated on the size of the bleeding event. X-ray computed tomography (CT) scans allow cli...

A novel model for predicting the outcome of intracerebral hemorrhage: Based on 1186 Patients.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
OBJECTIVE: To establish a model for predicting the outcome according to the clinical and computed tomography(CT) image data of patients with intracerebral hemorrhage(ICH).

Prediction of early neurological deterioration in acute minor ischemic stroke by machine learning algorithms.

Clinical neurology and neurosurgery
OBJECTIVES: A significant proportion of patients with acute minor stroke have unfavorable functional outcome due to early neurological deterioration (END). The purpose of this study was to evaluate the applicability of machine learning algorithms to ...

Exploring linearity of deep neural network trained QSM: QSMnet.

NeuroImage
Recently, deep neural network-powered quantitative susceptibility mapping (QSM), QSMnet, successfully performed ill-conditioned dipole inversion in QSM and generated high-quality susceptibility maps. In this paper, the network, which was trained by h...

Fidelity imposed network edit (FINE) for solving ill-posed image reconstruction.

NeuroImage
Deep learning (DL) is increasingly used to solve ill-posed inverse problems in medical imaging, such as reconstruction from noisy and/or incomplete data, as DL offers advantages over conventional methods that rely on explicit image features and hand ...

Machine learning models for identifying preterm infants at risk of cerebral hemorrhage.

PloS one
Intracerebral hemorrhage in preterm infants is a major cause of brain damage and cerebral palsy. The pathogenesis of cerebral hemorrhage is multifactorial. Among the risk factors are impaired cerebral autoregulation, infections, and coagulation disor...

Deep Learning for Automated Measurement of Hemorrhage and Perihematomal Edema in Supratentorial Intracerebral Hemorrhage.

Stroke
Background and Purpose- Volumes of hemorrhage and perihematomal edema (PHE) are well-established biomarkers of primary and secondary injury, respectively, in spontaneous intracerebral hemorrhage. An automated imaging pipeline capable of accurately an...