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Condition: Hemorrhagic Stroke
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Total 196 results found since Jan 2013.

Role of artificial intelligence and machine learning in the diagnosis of cerebrovascular disease
ConclusionThe implementation of AI and ML algorithms to supplement clinical practice has conferred a high degree of accuracy, efficiency, and expedited detection in the clinical and radiographic evaluation and management of ischemic and hemorrhagic strokes, AVMs, and aneurysms. Such algorithms have been explored for further purposes of prognostication for these conditions, with promising preliminary results. Further studies should evaluate the longitudinal implementation of such techniques into hospital networks and residency programs to supplement clinical practice, and the extent to which these techniques improve patient...
Source: Frontiers in Human Neuroscience - September 7, 2023 Category: Neuroscience Source Type: research

Interpretable machine learning for predicting 28-day all-cause in-hospital mortality for hypertensive ischemic or hemorrhagic stroke patients in the ICU: a multi-center retrospective cohort study with internal and external cross-validation
ConclusionsThe XGBoost model accurately predicted 28-day all-cause in-hospital mortality among hypertensive ischemic or hemorrhagic stroke patients admitted to the ICU. The SHAP method can provide explicit explanations of personalized risk prediction, which can aid physicians in understanding the model.
Source: Frontiers in Neurology - August 8, 2023 Category: Neurology Source Type: research

E-236 Automated pre- and post-operative volumes estimates risk of retreatment in chronic subdural hematoma: a retrospective, multi-center study
Conclusions/RelevanceLarger pre- and post-operative cSDH volumes increase the risk of cSDH retreatment. Volume thresholds may allow identification of patients at high risk of cSDH retreatment who would benefit from adjunct treatments. Machine learning algorithm can quickly provide accurate estimates of pre and post operative volumes.Disclosures J. Vargas: 2; C; Viz.AI, Synchron, Borvo, Imperative Care. 4; C; Viz.AI, Imperative Care, Cerenovus, Q’APel. M. Pease: None. M. Snyder: None. J. Blalock: None. S. Wu: None. E. Nwachuku: None. A. Mital: None. D. Okonkwo: None. R. Kellogg: 2; C; VizAI, Cerenovus, Imperative Care. 4; C; VizAI.
Source: Journal of NeuroInterventional Surgery - July 30, 2023 Category: Neurosurgery Authors: Vargas, J., Pease, M., Snyder, M., Blalock, J., Wu, S., Nwachuku, E., Mital, A., Okonkwo, D., Kellogg, R. Tags: SNIS 20th annual meeting electronic poster abstracts Source Type: research

Corrigendum: Ischemic and haemorrhagic stroke risk estimation using a machine-learning-based retinal image analysis
Source: Frontiers in Neurology - July 27, 2023 Category: Neurology Source Type: research

Deep-Learning-Enabled Microwave-Induced Thermoacoustic Tomography Based on ResAttU-Net for Transcranial Brain Hemorrhage Detection
Conclusion: The proposed ResAttU-Net-based DL-MITAT method is promising for mitigating the acoustic inhomogeneity issue and performing transcranial brain hemorrhage detection. Significance: This work provides a novel ResAttU-Net-based DL-MITAT paradigm and paves a compelling route for transcranial brain hemorrhage detection as well as other transcranial brain imaging applications.
Source: IEEE Transactions on Biomedical Engineering - July 21, 2023 Category: Biomedical Engineering 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

Delirium detection using wearable sensors and machine learning in patients with intracerebral hemorrhage
ConclusionsWe found that actigraphy in conjunction with machine learning models improves clinical detection of delirium in patients with stroke, thus paving the way to make actigraph-assisted predictions clinically actionable.
Source: Frontiers in Neurology - June 9, 2023 Category: Neurology Source Type: research

What Sub-Saharan African Nations Can Teach the U.S. About Black Maternal Health
While poor maternal outcomes among Black women in the U.S. is not new, improving it is imperative. U.S. policymakers can look to sub-Saharan Africa for guidance on reversing this trend. Credit: Ernest Ankomah/IPSBy Ifeanyi NsoforABUJA, Jun 2 2023 (IPS) New research shows that Black mothers in the United States disproportionately live in counties with higher maternal vulnerability and face greater risk of preterm death for the fetus, greater risk of low birth weight for a baby, and a higher number of maternal deaths. While poor maternal outcomes among Black women in the U.S. is not new, improving it is imperative. U.S. poli...
Source: IPS Inter Press Service - Health - June 2, 2023 Category: International Medicine & Public Health Authors: Ifeanyi Nsofor Tags: Africa Gender Headlines Health Inequality North America Poverty & SDGs Maternal Health Source Type: news

A brain CT-based approach for predicting and analyzing stroke-associated pneumonia from intracerebral hemorrhage
DiscussionOur findings suggest that our method is effective in classifying the development of pneumonia based on brain CT scans. Furthermore, we identified distinct characteristics, such as volume and distribution, of ICH in four different types of SAP.
Source: Frontiers in Neurology - June 2, 2023 Category: Neurology Source Type: research

Appropriate Use Criteria of Left Atrial Appendage Closure Devices: Latest Evidences
Expert Rev Med Devices. 2023 May 1. doi: 10.1080/17434440.2023.2208748. Online ahead of print.ABSTRACTAtrial fibrillation is the most common arrythmia and it is linked to an increased risk of stroke. Even if anticoagulation therapy reduces the rate of stroke the benefits of this therapy have to been balanced with the increased risk of hemorrhagic event. Left atrial appendage closure is a valid alternative to long term anticoagulation in patients with atrial fibrillation and high hemorrhagic risk. Actually new devices with different features have been tested and introduced progressively in the clinical practice. Improvement...
Source: Expert Review of Medical Devices - May 2, 2023 Category: Medical Devices Authors: Fabrizio Guarracini Eleonora Bonvicini Alberto Preda Marta Martin Simone Muraglia Giulia Casagranda Marianna Mochen Alessio Coser Silvia Quintarelli Stefano Branzoli Roberto Bonmassari Massimiliano Marini Patrizio Mazzone Source Type: research

What Are the Classifications of Perinatal Stroke?
Discussion Perinatal stroke occurs in about 1:1000 live births and is a “focal vascular injury from the fetal period to 28 days postnatal age.” Perinatal stroke is the most common cause of hemiparetic cerebral palsy and causes other significant morbidity including cognitive deficits, learning disabilities, motor problems, sensory problems including visual and hearing disorders, epilepsy, and behavioral and psychological problems. Family members are also affected because of the potential anxiety and guilt feelings that having a child with a stroke presents, along with the care that may be needed over the child&#...
Source: PediatricEducation.org - May 1, 2023 Category: Pediatrics Authors: Pediatric Education Tags: Uncategorized Source Type: news

Data lake-driven analytics identify nocturnal non-dipping of heart rate as predictor of unfavorable stroke outcome at discharge
ConclusionsOur data suggest that a lack of circadian HR modulation, specifically nocturnal non-dipping, is associated with short-term unfavorable functional outcome after stroke, and that including HR into machine learning-based prediction models may lead to improved stroke outcome prediction.
Source: Journal of Neurology - April 20, 2023 Category: Neurology Source Type: research

A clinical –radiomics model based on noncontrast computed tomography to predict hemorrhagic transformation after stroke by machine learning: a multicenter study
ConclusionThe proposed clinical –radiomics model is a dependable approach that could provide risk assessment of HT for patients who receive IVT after stroke.
Source: Insights into Imaging - March 29, 2023 Category: Radiology Source Type: research

Concurrent validity of machine learning-classified functional upper extremity use from accelerometry in chronic stroke
Conclusion: Our machine learning approach provides a valid measure of functional UE use. The accuracy, validity, and small footprint of this machine learning approach makes it feasible for measurement of UE recovery in stroke rehabilitation trials.
Source: Frontiers in Physiology - March 22, 2023 Category: Physiology Source Type: research