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The feasibility and accuracy of machine learning in improving safety and efficiency of thrombolysis for patients with stroke: Literature review and proposed improvements
In this study, we identified 29 related previous machine learning models, reviewed the models on the accuracy and feasibility, and proposed corresponding improvements. Regarding accuracy, lack of long-term outcome, treatment option consideration, and advanced radiological features were found in many previous studies in terms of model conceptualization. Regarding interpretability, most of the previous models chose restrictive models for high interpretability and did not mention processing time consideration. In the future, model conceptualization could be improved based on comprehensive neurological domain knowledge and fea...
Source: Frontiers in Neurology - October 20, 2022 Category: Neurology Source Type: research

Design and implementation of a Stroke Rehabilitation Registry for the systematic assessment of processes and outcomes and the development of data-driven prediction models: The STRATEGY study protocol
ConclusionsThis study will test the feasibility of a stroke rehabilitation registry in the Italian health context and provide a systematic assessment of processes and outcomes for quality assessment and benchmarking. By the development of data-driven prediction models in stroke rehabilitation, this study will pave the way for the development of decision support tools for patient-oriented therapy planning and rehabilitation outcomes maximization.Clinical tial registrationThe registration on ClinicalTrials.gov is ongoing and under review. The identification number will be provided when the review process will be completed.
Source: Frontiers in Neurology - October 10, 2022 Category: Neurology Source Type: research

Head-to-head comparison of commercial artificial intelligence solutions for detection of large vessel occlusion at a comprehensive stroke center
ConclusionBoth tools successfully detected most anterior circulation occlusions. RAPID LVO had higher sensitivity while CINA LVO had higher accuracy and specificity. Interestingly, both tools were able to detect some, but not all M2 MCA occlusions. This is the first study to compare traditional and deep learning LVO tools in the clinical setting.
Source: Frontiers in Neurology - October 10, 2022 Category: Neurology Source Type: research

Predicting futile recanalization, malignant cerebral edema, and cerebral herniation using intelligible ensemble machine learning following mechanical thrombectomy for acute ischemic stroke
ConclusionUsing the ensemble ML model to analyze the clinical and imaging data of AIS patients with successful recanalization at admission and within 24 h after MT allowed for accurately predicting the risks of futile recanalization, MCE, and CH.
Source: Frontiers in Neurology - September 28, 2022 Category: Neurology Source Type: research

Artificial intelligence for early stroke diagnosis in acute vestibular syndrome
ConclusionAI can accurately diagnose a vestibular stroke by using unprocessed vHIT time series. The quantification of eye- and head movements with the use of machine learning and AI can serve in the future for an automated diagnosis in ED patients with acute dizziness. The application of different neural network architectures can potentially further improve performance and enable direct inference from raw video recordings.
Source: Frontiers in Neurology - September 8, 2022 Category: Neurology Source Type: research

Ischemic and haemorrhagic stroke risk estimation using a machine-learning-based retinal image analysis
ConclusionA fast and fully automatic method can be used for stroke subtype risk assessment and classification based on fundus photographs alone.
Source: Frontiers in Neurology - August 22, 2022 Category: Neurology Source Type: research

Machine learning to predict futile recanalization of large vessel occlusion before and after endovascular thrombectomy
ConclusionsThe “Early” XGBoost and the “Late” XGBoost allowed us to predict futile recanalization before and after EVT accurately. Our study suggests that including peri-interventional characteristics may lead to superior predictive performance compared to a model based on baseline characteristics only. In addition, NIHSS after 24 h was the most important prognostic factor for futile recanalization.
Source: Frontiers in Neurology - August 19, 2022 Category: Neurology Source Type: research

Bimanual motor skill learning after stroke: Combining robotics and anodal tDCS over the undamaged hemisphere: An exploratory study
ConclusionA short motor skill learning session with a robotic device resulted in the retention and generalization of a complex skill involving bimanual cooperation. The tDCS strategy that would best enhance bimanual motor skill learning after stroke remains unknown.Clinical trial registrationhttps://clinicaltrials.gov/ct2/show/NCT02308852, identifier: NCT02308852.
Source: Frontiers in Neurology - August 18, 2022 Category: Neurology Source Type: research

Editorial: Machine Learning in Action: Stroke Diagnosis and Outcome Prediction
Source: Frontiers in Neurology - July 20, 2022 Category: Neurology Source Type: research

Machine Learning-Based Model for Prediction of Hemorrhage Transformation in Acute Ischemic Stroke After Alteplase
In this study, an RF machine learning method was successfully established to predict HT in AIS patients after intravenous alteplase, which the sensitivity was 66.7%, and the specificity was 80.7%.
Source: Frontiers in Neurology - June 10, 2022 Category: Neurology Source Type: research

“I Give It Everything for an Hour Then I Sleep for Four.” The Experience of Post-stroke Fatigue During Outpatient Rehabilitation Including the Perspectives of Carers: A Qualitative Study
ConclusionDespite engaging in outpatient rehabilitation, stroke survivors largely learnt to manage fatigue independent of healthcare professionals. Carers often facilitated learning, monitoring rehabilitation, daily routines and fatigue exacerbation. Conversely, family could be dismissive of fatigue and possess unrealistic expectations. Post-stroke fatigue must be considered by clinicians when delivering outpatient rehabilitation to stroke survivors. Clinicians should consistently screen for fatigue, provide flexible session scheduling, and educate about individual indicators and strategies for management. Clinicians shoul...
Source: Frontiers in Neurology - June 2, 2022 Category: Neurology Source Type: research

Comparing Poor and Favorable Outcome Prediction With Machine Learning After Mechanical Thrombectomy in Acute Ischemic Stroke
ConclusionsOur results suggest that a prediction of poor outcome after AIS and MT could be made based on clinical baseline variables only. Speed and extent of MT did improve prediction for a favorable outcome but is not relevant for poor outcome. An MR mismatch with small ischemic core and larger penumbral tissue showed no predictive importance.
Source: Frontiers in Neurology - May 27, 2022 Category: Neurology Source Type: research

Interpretable Machine Learning Modeling for Ischemic Stroke Outcome Prediction
ConclusionMachine learning that is applied to quantifiable image features from CT and CTA alongside basic clinical characteristics constitutes a promising automated method in the pre-interventional prediction of stroke prognosis. Interpretable models allow for exploring which initial features contribute the most to post-thrombectomy outcome prediction overall and for each individual patient outcome.
Source: Frontiers in Neurology - May 19, 2022 Category: Neurology Source Type: research

Knockdown of NRSF Alleviates Ischemic Brain Injury and Microvasculature Defects in Diabetic MCAO Mice
Diabetes is one of the well-established risk factors of stroke and is associated with a poor outcome in patients with stroke. Previous studies have shown that the expression of neuron restrictive silencer factor (NRSF) is elevated in diabetes as well as ischemic stroke. However, the role of NRSF in regulating an outcome of diabetic ischemic stroke has not been completely understood. Here, we hypothesized that diabetes-induced NRSF elevation can aggravate brain injury and cognition impairment in ischemic stroke. The diabetic ischemic stroke mice model was established by 8 weeks of high-fat-diet feeding and 5 days of strepto...
Source: Frontiers in Neurology - May 13, 2022 Category: Neurology Source Type: research

Combination of Radiological and Clinical Baseline Data for Outcome Prediction of Patients With an Acute Ischemic Stroke
ConclusionsThe combination of radiomics and deep learning image features with clinical data significantly improved the prediction of good reperfusion. The visualization of prediction feature importance showed both known and novel clinical and imaging features with predictive values.
Source: Frontiers in Neurology - April 1, 2022 Category: Neurology Source Type: research

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