Cerebral Hemorrhage

Identifying potential (re)hemorrhage among sporadic cerebral cavernous malformations using machine learning.

Scientific reports May 14, 2024

The (re)hemorrhage in patients with sporadic cerebral cavernous malformations (CCM) was the primary aim for CCM management. However, accurately identifying the potential (re)hemorrhage among sporadic CCM patients in advance remains a challenge. This ...

ROC Curve Support Vector Machine Cerebral Hemorrhage Hemangioma, Cavernous, Central Nervous System Humans Machine Learning Female Adult Male Middle Aged

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Predicting hematoma expansion using machine learning: An exploratory analysis of the ATACH 2 trial.

Journal of the neurological sciences May 12, 2024

INTRODUCTION: Hematoma expansion (HE) in patients with intracerebral hemorrhage (ICH) is a key predictor of poor prognosis and potentially amenable to treatment. This study aimed to build a classification model to predict HE in patients with ICH usin...

Humans Machine Learning Female Disease Progression Cerebral Hemorrhage Antihypertensive Agents Hematoma Aged Male Middle Aged

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Predicting early mortality and severe intraventricular hemorrhage in very-low birth weight preterm infants: a nationwide, multicenter study using machine learning.

Scientific reports May 12, 2024

Our aim was to develop a machine learning-based predictor for early mortality and severe intraventricular hemorrhage (IVH) in very-low birth weight (VLBW) preterm infants in Taiwan. We collected retrospective data from VLBW infants, dividing them int...

Male Infant Taiwan Cerebral Hemorrhage Retrospective Studies Machine Learning Female Infant, Newborn Prognosis Gestational Age Humans Infant, Premature, Diseases Birth Weight Cerebral Intraventricular Hemorrhage Infant Mortality Infant, Very Low Birth Weight Infant, Premature

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Application of deep learning and radiomics in the prediction of hematoma expansion in intracerebral hemorrhage: a fully automated hybrid approach.

Diagnostic and interventional radiology (Ankara, Turkey) Apr 24, 2024

PURPOSE: Spontaneous intracerebral hemorrhage (ICH) is the most severe form of stroke. The timely assessment of early hematoma enlargement and its proper treatment are of great significance in curbing the deterioration and improving the prognosis of ...

Cerebral Hemorrhage Hematoma Male Middle Aged Deep Learning Female Aged Radiomics Neural Networks, Computer Tomography, X-Ray Computed Humans

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Evolving Therapeutic Landscape of Intracerebral Hemorrhage: Emerging Cutting-Edge Advancements in Surgical Robots, Regenerative Medicine, and Neurorehabilitation Techniques.

Translational stroke research Apr 1, 2024

Intracerebral hemorrhage (ICH) is the most serious form of stroke and has limited available therapeutic options. As knowledge on ICH rapidly develops, cutting-edge techniques in the fields of surgical robots, regenerative medicine, and neurorehabilit...

Neurological Rehabilitation Regenerative Medicine Brain-Computer Interfaces Stem Cell Transplantation Humans Robotics Robotic Surgical Procedures Animals Cerebral Hemorrhage

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Automated Detection of Cerebral Microbleeds on Two-dimensional Gradient-recalled Echo T2* Weighted Images Using a Morphology Filter Bank and Convolutional Neural Network.

Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine Mar 15, 2024

PURPOSE: We present a novel algorithm for the automated detection of cerebral microbleeds (CMBs) on 2D gradient-recalled echo T2* weighted images (T2*WIs). This approach combines a morphology filter bank with a convolutional neural network (CNN) to i...

Algorithms Image Processing, Computer-Assisted Brain Magnetic Resonance Imaging Humans Middle Aged Retrospective Studies Aged, 80 and over Neural Networks, Computer Image Interpretation, Computer-Assisted Cerebral Hemorrhage Male Convolutional Neural Networks Female Aged

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Brain is also time: good short-term outcome predictions of artificial intelligence in spontaneous intracerebral hemorrhage pave the way for the long-term assessment.

European radiology Feb 23, 2024

Prognosis Humans Cerebral Hemorrhage Artificial Intelligence Brain

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Prediction of Hematoma Expansion in Intracerebral Hemorrhage in 24 Hours by Machine Learning Algorithm.

World neurosurgery Feb 22, 2024

OBJECTIVE: The significance of noncontrast computer tomography (CT) image markers in predicting hematoma expansion (HE) following intracerebral hemorrhage (ICH) within different time intervals in the initial 24 hours after onset may be uncertain. Hen...

Cerebral Hemorrhage Humans Predictive Value of Tests Machine Learning Aged Hematoma Tomography, X-Ray Computed Middle Aged Algorithms Male Female Disease Progression Retrospective Studies Time Factors

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Accuracy of automated segmentation and volumetry of acute intracerebral hemorrhage following minimally invasive surgery using a patch-based convolutional neural network in a small dataset.

Neuroradiology Feb 17, 2024

PURPOSE: In cases of acute intracerebral hemorrhage (ICH) volume estimation is of prognostic and therapeutic value following minimally invasive surgery (MIS). The ABC/2 method is widely used, but suffers from inaccuracies and is time consuming. Super...

Image Processing, Computer-Assisted Cerebral Hemorrhage Minimally Invasive Surgical Procedures Humans Aged Neural Networks, Computer Aged, 80 and over Male Machine Learning Middle Aged Tomography, X-Ray Computed

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Predicting hematoma expansion in acute spontaneous intracerebral hemorrhage: integrating clinical factors with a multitask deep learning model for non-contrast head CT.

Neuroradiology Feb 10, 2024

PURPOSE: To predict hematoma growth in intracerebral hemorrhage patients by combining clinical findings with non-contrast CT imaging features analyzed through deep learning.

Deep Learning Cerebral Hemorrhage Hematoma Humans Retrospective Studies Tomography, X-Ray Computed

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Explore the latest research on artificial intelligence and machine learning in medicine.

Identifying potential (re)hemorrhage among sporadic cerebral cavernous malformations using machine learning.

Predicting hematoma expansion using machine learning: An exploratory analysis of the ATACH 2 trial.

Predicting early mortality and severe intraventricular hemorrhage in very-low birth weight preterm infants: a nationwide, multicenter study using machine learning.

Application of deep learning and radiomics in the prediction of hematoma expansion in intracerebral hemorrhage: a fully automated hybrid approach.

Evolving Therapeutic Landscape of Intracerebral Hemorrhage: Emerging Cutting-Edge Advancements in Surgical Robots, Regenerative Medicine, and Neurorehabilitation Techniques.

Automated Detection of Cerebral Microbleeds on Two-dimensional Gradient-recalled Echo T2* Weighted Images Using a Morphology Filter Bank and Convolutional Neural Network.

Brain is also time: good short-term outcome predictions of artificial intelligence in spontaneous intracerebral hemorrhage pave the way for the long-term assessment.

Prediction of Hematoma Expansion in Intracerebral Hemorrhage in 24 Hours by Machine Learning Algorithm.

Accuracy of automated segmentation and volumetry of acute intracerebral hemorrhage following minimally invasive surgery using a patch-based convolutional neural network in a small dataset.

Predicting hematoma expansion in acute spontaneous intracerebral hemorrhage: integrating clinical factors with a multitask deep learning model for non-contrast head CT.

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