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Machine learning-based prediction of clinical outcomes after first-ever ischemic stroke
ConclusionOur machine learning analysis successfully demonstrated the ability to predict clinical outcomes after first-ever ischemic stroke and identified the leading prognostic factors that contribute to this prediction.
Source: Frontiers in Neurology - February 21, 2023 Category: Neurology Source Type: research

The predictors of death within 1 year in acute ischemic stroke patients based on machine learning
ConclusionsThe network calculator based on the XGB model developed in this study can help clinicians make more personalized and rational clinical decisions.
Source: Frontiers in Neurology - February 23, 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

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

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

A Paradigm Shift: Rehabilitation Robotics, Cognitive Skills Training, and Function After Stroke
Conclusion: The ALPS protocol was the first to extend cognitive strategy training to robot-assisted therapy. The intervention in this development of concept pilot trial was feasible and well-tolerated, with good potential to optimize paretic UE performance following robot-assisted therapy.
Source: Frontiers in Neurology - October 14, 2019 Category: Neurology Source Type: research

Quantifying the Impact of Chronic Ischemic Injury on Clinical Outcomes in Acute Stroke With Machine Learning
Acute stroke is often superimposed on chronic damage from previous cerebrovascular events. This background will inevitably modulate the impact of acute injury on clinical outcomes to an extent that will depend on the precise anatomical pattern of damage. Previous attempts to quantify such modulation have employed only reductive models that ignore anatomical detail. The combination of automated image processing, large-scale data, and machine learning now enables us to quantify the impact of this with high-dimensional multivariate models sensitive to individual variations in the detailed anatomical pattern. We introduce and ...
Source: Frontiers in Neurology - January 23, 2020 Category: Neurology Source Type: research

Predicting Poor Outcome Before Endovascular Treatment in Patients With Acute Ischemic Stroke
Conclusions: All prediction models showed a high AUC. The best-performing models correctly identified 34% of the poor outcome patients at a cost of misclassifying 4% of non-poor outcome patients. Further studies are necessary to determine whether these accuracies are reproducible before implementation in clinical practice.
Source: Frontiers in Neurology - October 15, 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

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

Time Course and Mechanisms Underlying Standing Balance Recovery Early After Stroke: Design of a Prospective Cohort Study With Repeated Measurements
DiscussionThe current study aims to investigate how stroke survivors “re-learn” to maintain standing balance as an integral part of daily life activities. The knowledge gained through this study may contribute to recommending treatment strategies for early stroke rehabilitation targeting behavioral restitution of the most-affected leg or learning to compensate with the less-affected leg.
Source: Frontiers in Neurology - February 21, 2022 Category: Neurology Source Type: research

Deep Learning-Enabled Clinically Applicable CT Planbox for Stroke With High Accuracy and Repeatability
ConclusionsCAPITAL-CT generated standard and reproducible images that could simplify the work of radiologists, which would be of great help in the follow-up of stroke patients and in multifield research in neuroscience.
Source: Frontiers in Neurology - March 11, 2022 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

Impact of 25-Hydroxyvitamin D on the Prognosis of Acute Ischemic Stroke: Machine Learning Approach
Conclusions: 25-hydroxyvitamin D deficiency was highly prevalent in Korea and low 25-hydroxyvitamin D level was associated with poor outcome in patients with AIS. The machine learning approach of extreme gradient boosting was also useful to assess stroke prognosis along with logistic regression analysis.
Source: Frontiers in Neurology - January 30, 2020 Category: Neurology Source Type: research

Machine Learning Analysis of the Cerebrovascular Thrombi Proteome in Human Ischemic Stroke: An Exploratory Study
Conclusion: Our results advance the portrayal of the human cerebrovascular thrombi proteome. The exploratory SVM analysis outlined sets of proteins for a proof-of-principle characterization of our cohort cardioembolic and atherothrombotic samples. The integrated analysis proposed herein could be further developed and retested on a larger patients population to better understand stroke origin and the associated cerebrovascular pathophysiology.
Source: Frontiers in Neurology - November 5, 2020 Category: Neurology Source Type: research

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