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Cerebral Hemorrhage

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Deep-learning measurement of intracerebral haemorrhage with mixed precision training: a coarse-to-fine study.

Clinical radiology
AIM: To develop a unified deep-learning-based method for automated intracerebral haemorrhage (ICH) segmentation on computed tomography (CT) images with different layer thickness parameters.

A deep learning model for prognosis prediction after intracranial hemorrhage.

Journal of neuroimaging : official journal of the American Society of Neuroimaging
BACKGROUND AND PURPOSE: Intracranial hemorrhage (ICH) is a common life-threatening condition that must be rapidly diagnosed and treated. However, there is still a lack of consensus regarding treatment, driven to some extent by prognostic uncertainty....

A machine learning approach for predicting perihematomal edema expansion in patients with intracerebral hemorrhage.

European radiology
OBJECTIVES: Preventing the expansion of perihematomal edema (PHE) represents a novel strategy for the improvement of neurological outcomes in intracerebral hemorrhage (ICH) patients. Our goal was to predict early and delayed PHE expansion using a mac...

Predicting prognosis of primary pontine hemorrhage using CT image and deep learning.

NeuroImage. Clinical
Prognosis of primary pontine hemorrhage (PPH) is important for treatment planning and patient management. However, only few clinical factors were reported to have prognostic value to PPH. Here, we propose a deep learning (DL) model that mines high-di...

Joint modeling strategy for using electronic medical records data to build machine learning models: an example of intracerebral hemorrhage.

BMC medical informatics and decision making
BACKGROUND: Outliers and class imbalance in medical data could affect the accuracy of machine learning models. For physicians who want to apply predictive models, how to use the data at hand to build a model and what model to choose are very thorny p...

Symmetry-Aware Deep Learning for Cerebral Ventricle Segmentation With Intra-Ventricular Hemorrhage.

IEEE journal of biomedical and health informatics
Cerebral ventricles are one of the prominent structures in the brain, segmenting which can provide rich information for brain-related disease diagnosis. Unfortunately, cerebral ventricle segmentation in complex clinical cases, such as in the coexiste...

Predictive nomogram for soft robotic hand rehabilitation of patients with intracerebral hemorrhage.

BMC neurology
BACKGROUND: Few studies focused on the risk factors for hand rehabilitation of intracerebral hemorrhage (ICH) using of soft robotic hand therapy (SRHT). The aim of this study was to establish a predictive nomogram for soft robotic hand rehabilitation...

A Continuum Robotic Cannula With Tip Following Capability and Distal Dexterity for Intracerebral Hemorrhage Evacuation.

IEEE transactions on bio-medical engineering
OBJECTIVE: This paper aims to investigate a new continuum robot design and its motion implementation methods appropriate for a minimally invasive intracerebral hemorrhage (ICH) evacuation.

Accuracy of artificial intelligence for the detection of intracranial hemorrhage and chronic cerebral microbleeds: a systematic review and pooled analysis.

La Radiologia medica
BACKGROUND: Artificial intelligence (AI)-driven software has been developed and become commercially available within the past few years for the detection of intracranial hemorrhage (ICH) and chronic cerebral microbleeds (CMBs). However, there is curr...

Convolutional Neural Network in Microsurgery Treatment of Spontaneous Intracerebral Hemorrhage.

Computational and mathematical methods in medicine
OBJECTIVE: To explore the convolutional neural network (CNN) method in measuring hematoma volume-assisted microsurgery for spontaneous cerebral hemorrhage.