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Condition: Hemorrhagic Stroke
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Total 247 results found since Jan 2013.

Machine Learning-Enabled Determination of Diffuseness of Brain Arteriovenous Malformations from Magnetic Resonance Angiography
AbstractThe diffuseness of brain arteriovenous malformations (bAVMs) is a significant factor in surgical outcome evaluation and hemorrhagic risk prediction. However, there are still predicaments in identifying diffuseness, such as the judging variety resulting from different experience and difficulties in quantification. The purpose of this study was to develop a machine learning (ML) model to automatically identify the diffuseness of bAVM niduses using three-dimensional (3D) time-of-flight magnetic resonance angiography (TOF-MRA) images. A total of 635 patients with bAVMs who underwent TOF-MRA imaging were enrolled. Three...
Source: Translational Stroke Research - August 12, 2021 Category: Neurology Source Type: research

CT-based radiomics for differentiating intracranial contrast extravasation from intraparenchymal haemorrhage after mechanical thrombectomy
ConclusionsThe radiomic signature constructed based on initial post-operative nonenhanced CT after mechanical thrombectomy can effectively differentiate IPH from iodinated contrast extravasation.Key Points•Radiomic features were extracted from intraparenchymal areas of hyperattenuation on initial post-operative CT scans after mechanical thrombectomy.•The nonenhanced CT-based radiomic signature can differentiate IPH from iodinated contrast extravasation early.•The radiomic signature may help prevent unnecessary rescanning after mechanical thrombectomy, especially in cases where contrast extravasation is highly suggestive.
Source: European Radiology - February 3, 2022 Category: Radiology Source Type: research

A Nomogram Model for Predicting Type-2 Myocardial Infarction Induced by Acute Upper Gastrointestinal Bleeding
ConclusionThe nomogram was proven to be a useful tool for the risk stratification of T2MI in AUGIB patients, and is helpful for the early identification of AUGIB patients who are prone to T2MI for early intervention, especially in emergency departments and intensive care units.
Source: Journal of Huazhong University of Science and Technology -- Medical Sciences -- - March 15, 2022 Category: Research Source Type: research

ANAID-ICH nomogram for predicting unfavorable outcome after intracerebral hemorrhage
CONCLUSIONS: The ANAID-ICH nomogram comprising age, NIHSS score, anemia, infratentorial location, presence of DWILs, and prior ICH may facilitate the identification of patients at higher risk for an unfavorable outcome.PMID:36000537 | DOI:10.1111/cns.13941
Source: CNS Neuroscience and Therapeutics - August 24, 2022 Category: Neuroscience Authors: Jiawen Li Dong Luo Feifei Peng Qi Kong Huawei Liu Meiyuan Chen Lusha Tong Feng Gao Source Type: research

Prediction of bleb formation in intracranial aneurysms using machine learning models based on aneurysm hemodynamics, geometry, location, and patient population
Conclusions Based on the premise that aneurysm characteristics prior to bleb formation resemble those derived from vascular reconstructions with their blebs virtually removed, machine learning models can identify aneurysms prone to bleb development with good accuracy. Pending further validation with longitudinal data, these models may prove valuable for assessing the propensity of IAs to progress to vulnerable states and potentially rupturing.
Source: Journal of NeuroInterventional Surgery - September 14, 2022 Category: Neurosurgery Authors: Salimi Ashkezari, S. F., Mut, F., Slawski, M., Cheng, B., Yu, A. K., White, T. G., Woo, H. H., Koch, M. J., Amin-Hanjani, S., Charbel, F. T., Rezai Jahromi, B., Niemelä, M., Koivisto, T., Frosen, J., Tobe, Y., Maiti, S., Robertson, A. M., Cebral, Tags: Hemorrhagic stroke Source Type: research

Lactate-to-albumin ratio is associated with in-hospital mortality in patients with spontaneous subarachnoid hemorrhage and a nomogram model construction
ConclusionLAR is closely associated with increased in-hospital mortality of patients with spontaneous SAH, which could serve as a novel clinical marker. The nomogram model combined with LAR, APSIII, age, and anion gap presents good predictive performance and clinical practicability.
Source: Frontiers in Neurology - October 17, 2022 Category: Neurology Source Type: research

More is less: Effect of ICF-based early progressive mobilization on severe aneurysmal subarachnoid hemorrhage in the NICU
DiscussionWe conclude that the ICF-based early progressive mobilization protocol is an effective and feasible intervention tool. For validity, more mobilization interventions might lead to less pneumonia, duration of mechanical ventilation and length of stay for patients with severe aSAH in the NICU, Moreover, it is necessary to pay attention over potential adverse events (especially line problems), although we did not find serious safety events.
Source: Frontiers in Neurology - December 14, 2022 Category: Neurology Source Type: research

Radiomics assessment of carotid intraplaque hemorrhage: detecting the vulnerable patients
ConclusionsA CT-based radiomics nomogram showed satisfactory performance in distinguishing carotid plaques with and without intraplaque hemorrhage.
Source: Insights into Imaging - December 20, 2022 Category: Radiology Source Type: research

Identifying vulnerable plaques: A 3D carotid plaque radiomics model based on HRMRI
ConclusionsHRMRI-based 3D carotid radiomics models can improve the precision of detecting vulnerable carotid plaques, consequently improving risk classification and clinical decision-making in patients with carotid stenosis.
Source: Frontiers in Neurology - January 26, 2023 Category: Neurology Source Type: research

Pattern of Neurological Disorders among Patients Evaluated in the Emergency Department; Cross-Sectional Study
CONCLUSION: In our study, neurologic emergencies accounted for 3.7% of all emergency admissions. Stroke, epileptic seizures, cerebral venous thrombosis, encephalopathies, and acute spinal cord diseases were the most common neurological disorders. The admission rate was very high following neurologic assessment by neurologists.PMID:36743701 | PMC:PMC9887228 | DOI:10.22037/aaem.v11i1.1813
Source: Accident and Emergency Nursing - February 6, 2023 Category: Emergency Medicine Authors: Mohamed Sheikh Hassan Nor Osman Sidow Alper G ökgül Bakar Ali Adam Mohamed Farah Osman Hussein Hassan Mohamed Ismail Gedi Ibrahim Ishak Ahmed Abdi Source Type: research

Identifying vulnerable plaques: A 3D carotid plaque radiomics model based on HRMRI
CONCLUSIONS: HRMRI-based 3D carotid radiomics models can improve the precision of detecting vulnerable carotid plaques, consequently improving risk classification and clinical decision-making in patients with carotid stenosis.PMID:36779063 | PMC:PMC9908750 | DOI:10.3389/fneur.2023.1050899
Source: Atherosclerosis - February 13, 2023 Category: Cardiology Authors: Xun Zhang Zhaohui Hua Rui Chen Zhouyang Jiao Jintao Shan Chong Li Zhen Li Source Type: research

CT Angiography Radiomics Combining Traditional Risk Factors to Predict Brain Arteriovenous Malformation Rupture: a Machine Learning, Multicenter Study
This study aimed to develop a machine learning model for predicting brain arteriovenous malformation (bAVM) rupture using a combination of traditional risk factors and radiomics features. This multicenter retrospective study enrolled 586 patients with unruptured bAVMs from 2010 to 2020. All patients were grouped into the hemorrhage (n = 368) and non-hemorrhage (n = 218) groups. The bAVM nidus were segmented on CT angiography images using Slicer software, and radiomic features were extracted using Pyradiomics. The dataset included a training set and an independent testing set. The machine learning model was developed on the...
Source: Translational Stroke Research - June 13, 2023 Category: Neurology Source Type: research