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Assessing the Relative Value of CT Perfusion Compared to Non-contrast CT and CT Angiography in Prognosticating Reperfusion-Eligible Acute Ischemic Stroke Patients
In the present study we sought to measure the relative statistical value of various multimodal CT protocols at identifying treatment responsiveness in patients being considered for thrombolysis. We used a prospectively collected cohort of acute ischemic stroke patients being assessed for IV-alteplase, who had CT-perfusion (CTP) and CT-angiography (CTA) before a treatment decision. Linear regression and receiver operator characteristic curve analysis were performed to measure the prognostic value of models incorporating each imaging modality. One thousand five hundred and sixty-two sub-4.5 h ischemic stroke patients were in...
Source: Frontiers in Neurology - September 9, 2021 Category: Neurology Source Type: research

Prediction of 30-Day Readmission After Stroke Using Machine Learning and Natural Language Processing
Conclusion: NLP-enhanced machine learning models potentially advance our ability to predict readmission after stroke. However, further improvement is necessary before being implemented in clinical practice given the weak discrimination.
Source: Frontiers in Neurology - July 13, 2021 Category: Neurology Source Type: research

Diagnosis of Acute Central Dizziness With Simple Clinical Information Using Machine Learning
Conclusions: Machine learning is feasible for classifying central dizziness using demographics, risk factors, vital signs, and clinical dizziness presentation, which are obtainable at the triage.
Source: Frontiers in Neurology - July 12, 2021 Category: Neurology Source Type: research

Prediction of Progression to Severe Stroke in Initially Diagnosed Anterior Circulation Ischemic Cerebral Infarction
Conclusion: The U-Net was fully automatic and showed a high correlation with manual segmentation. An integrated approach combining clinical variables and stroke lesion volumes that were derived from the advanced machine learning algorithms had high accuracy in predicting the progression to severe stroke in ASACNLII patients.
Source: Frontiers in Neurology - June 18, 2021 Category: Neurology Source Type: research

Behavioral Assessment of Sensory, Motor, Emotion, and Cognition in Rodent Models of Intracerebral Hemorrhage
Intracerebral hemorrhage (ICH) is the second most common type of stroke and has one of the highest fatality rates of any disease. There are many clinical signs and symptoms after ICH due to brain cell injury and network disruption resulted from the rupture of a tiny artery and activation of inflammatory cells, such as motor dysfunction, sensory impairment, cognitive impairment, and emotional disturbance, etc. Thus, researchers have established many tests to evaluate behavioral changes in rodent ICH models, in order to achieve a better understanding and thus improvements in the prognosis for the clinical treatment of stroke...
Source: Frontiers in Neurology - June 17, 2021 Category: Neurology Source Type: research

Assessment Methods of Post-stroke Gait: A Scoping Review of Technology-Driven Approaches to Gait Characterization and Analysis
This study aimed to review and summarize research efforts applicable to quantification and analyses of post-stroke gait with focus on recent technology-driven gait characterization and analysis approaches, including the integration of smart low cost wearables and Artificial Intelligence (AI), as well as feasibility and potential value in clinical settings.Methods: A comprehensive literature search was conducted within Google Scholar, PubMed, and ScienceDirect using a set of keywords, including lower extremity, walking, post-stroke, and kinematics. Original articles that met the selection criteria were included.Results and ...
Source: Frontiers in Neurology - June 8, 2021 Category: Neurology Source Type: research

Machine Learning-Based Prediction of Brain Tissue Infarction in Patients With Acute Ischemic Stroke Treated With Theophylline as an Add-On to Thrombolytic Therapy: A Randomized Clinical Trial Subgroup Analysis
Conclusions: The predicted follow-up brain lesions for each patient were not significantly different for patients virtually treated with theophylline or placebo, as an add-on to thrombolytic therapy. Thus, this study confirmed the lack of neuroprotective effect of theophylline shown in the main clinical trial and is contrary to the results from preclinical stroke models.
Source: Frontiers in Neurology - May 21, 2021 Category: Neurology Source Type: research

Prediction of Clinical Outcomes in Acute Ischaemic Stroke Patients: A Comparative Study
Conclusion: Aggregating clinical and imaging information leads to considerably better outcome prediction models. While lesion-symptom mapping is a useful tool to investigate structure-function relationships of the brain, it does not lead to better outcome predictions compared to a simple data-driven feature selection approach, which is less computationally expensive and easier to implement.
Source: Frontiers in Neurology - May 6, 2021 Category: Neurology Source Type: research

Does Cathodal vs. Sham Transcranial Direct Current Stimulation Over Contralesional Motor Cortex Enhance Upper Limb Motor Recovery Post-stroke? A Systematic Review and Meta-analysis
Conclusions: The effects of cathodal tDCS to contralesional M1 on motor recovery are small and consistent. There may be sub-populations that may respond to this approach; however, further research with larger cohorts is required.
Source: Frontiers in Neurology - April 15, 2021 Category: Neurology Source Type: research

Predictors of Function, Activity, and Participation of Stroke Patients Undergoing Intensive Rehabilitation: A Multicenter Prospective Observational Study Protocol
Discussion: By identifying data-driven prognosis prediction models in stroke rehabilitation, this study might contribute to the development of patient-oriented therapy and to optimize rehabilitation outcomes.Clinical Trial Registration:ClinicalTrials.gov, NCT03968627. https://www.clinicaltrials.gov/ct2/show/NCT03968627?term=Cecchi&cond=Stroke&draw=2&rank=2.
Source: Frontiers in Neurology - April 8, 2021 Category: Neurology Source Type: research

Machine Learning-Enabled 30-Day Readmission Model for Stroke Patients
Conclusions: Machine learning-based models can be designed to predict 30-day readmission after stroke using structured data from EHR. Among the algorithms analyzed, XGBoost with ROSE-sampling had the best performance in terms of AUC while LR with ROSE-sampling and feature selection had the best sensitivity. Clinical variables highly associated with 30-day readmission could be targeted for personalized interventions. Depending on healthcare systems' resources and criteria, models with optimized performance metrics can be implemented to improve outcomes.
Source: Frontiers in Neurology - March 31, 2021 Category: Neurology Source Type: research

Exploring How Low Oxygen Post Conditioning Improves Stroke-Induced Cognitive Impairment: A Consideration of Amyloid-Beta Loading and Other Mechanisms
Cognitive impairment is a common and disruptive outcome for stroke survivors, which is recognized to be notoriously difficult to treat. Previously, we have shown that low oxygen post-conditioning (LOPC) improves motor function and limits secondary neuronal loss in the thalamus after experimental stroke. There is also emerging evidence that LOPC may improve cognitive function post-stroke. In the current study we aimed to explore how exposure to LOPC may improve cognition post-stroke. Experimental stroke was induced using photothrombotic occlusion in adult, male C57BL/6 mice. At 72 h post-stroke animals were randomly assigne...
Source: Frontiers in Neurology - March 24, 2021 Category: Neurology Source Type: research

Deep Learning-Based Screening Test for Cognitive Impairment Using Basic Blood Test Data for Health Examination
Conclusions: The DNN model could predict cognitive function accurately. The predicted MMSE scores were significantly lower than the ground truth scores in the Healthy and Health examination groups, while there was no significant difference in the Patient group. We suggest that the difference between the predicted and ground truth MMSE scores was caused by changes in atherosclerosis with aging, and that applying the DNN model to younger subjects may predict future cognitive impairment after the onset of atherosclerosis.
Source: Frontiers in Neurology - December 14, 2020 Category: Neurology Source Type: research

Sensorimotor vs. Motor Upper Limb Therapy for Patients With Motor and Somatosensory Deficits: A Randomized Controlled Trial in the Early Rehabilitation Phase After Stroke
Conclusion: UL motor therapy may improve motor impairment more than UL sensorimotor therapy in patients with sensorimotor impairments in the early rehabilitation phase post stroke. For these patients, integrated sensorimotor therapy may not improve somatosensory function and may be less effective for motor recovery.Clinical Trial Registration:www.ClinicalTrials.gov, identifier NCT03236376.
Source: Frontiers in Neurology - December 4, 2020 Category: Neurology Source Type: research

Predicting 6-Month Unfavorable Outcome of Acute Ischemic Stroke Using Machine Learning
Conclusions: Compared with the HIAT score, the THRIVE score, and the NADE nomogram, the RFC model can improve the prediction of 6-month outcome in Chinese AIS patients.
Source: Frontiers in Neurology - November 19, 2020 Category: Neurology Source Type: research

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