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Source: Frontiers in Neurology
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Total 103 results found since Jan 2013.
Role of hypoxic exosomes and the mechanisms of exosome release in the CNS under hypoxic conditions
Hypoxia is characterized by low oxygen levels in the body or environment, resulting in various physiological and pathological changes. The brain, which has the highest oxygen consumption of any organ, is particularly susceptible to hypoxic injury. Exposure to low-pressure hypoxic environments can cause irreversible brain damage. Hypoxia can occur in healthy individuals at high altitudes or in pathological conditions such as trauma, stroke, inflammation, and autoimmune and neurodegenerative diseases, leading to severe brain damage and impairments in cognitive, learning, and memory functions. Exosomes may play a role in the ...
Source: Frontiers in Neurology - September 15, 2023 Category: Neurology Source Type: research
Interpretable machine learning for prediction of clinical outcomes in acute ischemic stroke
ConclusionML algorithms demonstrated proficient prediction for the 3-month functional outcome in AIS patients. With the aid of the SHAP method, we can attain an in-depth understanding of how critical features contribute to model predictions and how changes in these features influence such predictions.
Source: Frontiers in Neurology - September 7, 2023 Category: Neurology Source Type: research
Editorial: Machine learning in data analysis for stroke/endovascular therapy
Source: Frontiers in Neurology - August 24, 2023 Category: Neurology 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
Machine learning in the prediction of post-stroke cognitive impairment: a systematic review and meta-analysis
ConclusionML is a potential tool for predicting PSCI and may be used to develop simple clinical scoring scales for subsequent clinical use.Systematic Review Registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?RecordID=383476.
Source: Frontiers in Neurology - August 3, 2023 Category: Neurology 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
Identification of a miRNA –mRNA regulatory network for post-stroke depression: a machine-learning approach
ConclusionThe study highlighted gene signatures for PSD with three genes (SPATA2, ZNF208, and YTHDC1) and four upstream miRNAs (miR-6883-5p, miR-6873-3p, miR-4776-3p, and miR-6738-3p). These biomarkers could further our understanding of the pathogenesis of PSD.
Source: Frontiers in Neurology - July 17, 2023 Category: Neurology Source Type: research
Connectomic insight into unique stroke patient recovery after rTMS treatment
This study demonstrates for the first time the feasibility of using individualized connectivity analyses in differentiating unique phenotypes in rTMS treatment responses and recovery. This personalized connectomic approach may be utilized in the future to better understand patient recovery trajectories with neuromodulatory treatment.
Source: Frontiers in Neurology - July 6, 2023 Category: Neurology Source Type: research
OEDL: an optimized ensemble deep learning method for the prediction of acute ischemic stroke prognoses using union features
ConclusionThe OEDL approach proposed herein could effectively achieve improved stroke prognosis prediction performance, the effect of using combined data modeling was significantly better than that of single clinical or radiomics feature models, and the proposed method had a better intervention guidance value. Our approach is beneficial for optimizing the early clinical intervention process and providing the necessary clinical decision support for personalized treatment.
Source: Frontiers in Neurology - June 21, 2023 Category: Neurology Source Type: research
Computed tomography angiography-based radiomics model for predicting carotid atherosclerotic plaque vulnerability
This study aimed to identify radiomic features associated with the neovascularization of CAP and construct a prediction model for CAP vulnerability based on radiomic features. CTA data and clinical data of patients with CAPs who underwent CTA and CEUS between January 2018 and December 2021 in Beijing Hospital were retrospectively collected. The data were divided into a training cohort and a testing cohort using a 7:3 split. According to the examination of CEUS, CAPs were dichotomized into vulnerable and stable groups. 3D Slicer software was used to delineate the region of interest in CTA images, and the Pyradiomics package...
Source: Frontiers in Neurology - June 16, 2023 Category: Neurology Source Type: research
A blended neurostimulation protocol to delineate cortico-muscular and spino-muscular dynamics following neuroplastic adaptation
In this paper we propose a novel neurostimulation protocol that provides an intervention-based assessment to distinguish the contributions of different motor control networks in the cortico-spinal system. Specifically, we use a combination of non-invasive brain stimulation and neuromuscular stimulation to probe neuromuscular system behavior with targeted impulse-response system identification. In this protocol, we use an in-house developed human-machine interface (HMI) for an isotonic wrist movement task, where the user controls a cursor on-screen. During the task, we generate unique motor evoked potentials based on trigge...
Source: Frontiers in Neurology - June 15, 2023 Category: Neurology Source Type: research
Machine learning prediction of motor function in chronic stroke patients: a systematic review and meta-analysis
ConclusionML can be used as an assessment tool for predicting the motor function in patients with 3–6 months of post-stroke. Additionally, the study found that ML models with radiomics as a predictive variable were also demonstrated to have good predictive capabilities. This systematic review provides valuable guidance for the future optimization of ML prediction systems that predict poor motor outcomes in stroke patients.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022335260, identifier: CRD42022335260.
Source: Frontiers in Neurology - June 13, 2023 Category: Neurology Source Type: research
Predicting DWI-FLAIR mismatch on NCCT: the role of artificial intelligence in hyperacute decision making
ConclusionThe DWI-FLAIR mismatch may be reckoned using NCCT images through advanced artificial intelligence techniques.
Source: Frontiers in Neurology - June 12, 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
Prediction of subjective cognitive decline after corpus callosum infarction by an interpretable machine learning-derived early warning strategy
ConclusionOur study firstly demonstrated that the LR-model with 9 common variables has the best-performance to predict the risk of post-stroke SCD due to CC infarcton. Particularly, the combination of LR-model and SHAP-explainer could aid in achieving personalized risk prediction and be served as a decision-making tool for early intervention since its poor long-term outcome.
Source: Frontiers in Neurology - June 9, 2023 Category: Neurology Source Type: research
