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Machine Learning-Based Model for Predicting Incidence and Severity of Acute Ischemic Stroke in Anterior Circulation Large Vessel Occlusion
Conclusions: Machine learning methods with multiple clinical variables have the ability to predict acute ischemic stroke and the severity of neurological impairment in patients with AC-LVO.
Source: Frontiers in Neurology - December 2, 2021 Category: Neurology Source Type: research

Machine Learning in Action: Stroke Diagnosis and Outcome Prediction
This article provides an overview of machine learning technology and a tabulated review of pertinent machine learning studies related to stroke diagnosis and outcome prediction.
Source: Frontiers in Neurology - December 6, 2021 Category: Neurology Source Type: research

Effects of Repetitive Peripheral Sensory Stimulation in the Subacute and Chronic Phases After Stroke: Study Protocol for a Pilot Randomized Trial
DiscussionThe results of this study are relevant to inform future clinical trials to tailor RPSS to patients more likely to benefit from this intervention.Trial RegistrationNCT03956407.
Source: Frontiers in Neurology - February 16, 2022 Category: Neurology Source Type: research

Leveraging Factors of Self-Efficacy and Motivation to Optimize Stroke Recovery
The International Classification of Functioning, Disability and Health framework recognizes that an individual's functioning post-stroke reflects an interaction between their health condition and contextual factors encompassing personal and environmental factors. Personal factors significantly impact rehabilitation outcomes as they determine how an individual evaluates their situation and copes with their condition in daily life. A key personal factor is self-efficacy—an individual's belief in their capacity to achieve certain outcomes. Self-efficacy influences an individual's motivational state to execute behaviors nece...
Source: Frontiers in Neurology - February 24, 2022 Category: Neurology Source Type: research

Bridging the Transient Intraluminal Stroke Preclinical Model to Clinical Practice: From Improved Surgical Procedures to a Workflow of Functional Tests
Acute ischemic stroke (AIS) remains a leading cause of mortality, despite significant advances in therapy (endovascular thrombectomy). Failure in developing novel effective therapies is associated with unsuccessful translation from preclinical studies to clinical practice, associated to inconsistent and highly variable infarct areas and lack of relevant post-stroke functional evaluation in preclinical research. To outreach these limitations, we optimized the intraluminal transient middle cerebral occlusion, a widely used mouse stroke model, in two key parameters, selection of appropriate occlusion filaments and time of occ...
Source: Frontiers in Neurology - March 11, 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

Alternative Motor Task-Based Pattern Training With a Digital Mirror Therapy System Enhances Sensorimotor Signal Rhythms Post-stroke
Mirror therapy (MT) facilitates motor learning and induces cortical reorganization and motor recovery from stroke. We applied the new digital mirror therapy (DMT) system to compare the cortical activation under the three visual feedback conditions: (1) no mirror visual feedback (NoMVF), (2) bilateral synchronized task-based mirror visual feedback training (BMVF), and (3) reciprocal task-based mirror visual feedback training (RMVF). During DMT, EEG recordings, including time-dependent event-related desynchronization (ERD) signal amplitude in both mu and beta bands, were obtained from the standard C3 (ispilesional hemisphere...
Source: Frontiers in Neurology - November 21, 2019 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

Analyzing and predicting the risk of death in stroke patients using machine learning
ConclusionWe used several highly interpretive machine learning models to predict stroke prognosis with the highest accuracy to date and to identify heterogeneous treatment effects of warfarin and human albumin in stroke patients. Our interpretation of the model yielded a number of findings that are consistent with clinical knowledge and warrant further study and verification.
Source: Frontiers in Neurology - February 3, 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

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

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

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

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

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

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