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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

Clustering and prediction of long-term functional recovery patterns in first-time stroke patients
ConclusionsThe longitudinal, multi-dimensional, functional assessment data of first-time stroke patients were successfully clustered, and the prediction models showed relatively good accuracies. Early identification and prediction of long-term functional outcomes will help clinicians develop customized treatment strategies.
Source: Frontiers in Neurology - March 8, 2023 Category: Neurology Source Type: research

Application of Machine Learning Techniques to Identify Data Reliability and Factors Affecting Outcome After Stroke Using Electronic Administrative Records
Conclusion: Electronic administrative records from this cohort produced reliable outcome prediction and identified clinically appropriate factors negatively impacting most outcome variables following hospital admission with stroke. This presents a means of future identification of modifiable factors associated with patient discharge destination. This may potentially aid in patient selection for certain interventions and aid in better patient and clinician education regarding expected discharge outcomes.
Source: Frontiers in Neurology - September 27, 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

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

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

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-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

A brain CT-based approach for predicting and analyzing stroke-associated pneumonia from intracerebral hemorrhage
DiscussionOur findings suggest that our method is effective in classifying the development of pneumonia based on brain CT scans. Furthermore, we identified distinct characteristics, such as volume and distribution, of ICH in four different types of SAP.
Source: Frontiers in Neurology - June 2, 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

Machine learning approach for hemorrhagic transformation prediction: Capturing predictors' interaction
ConclusionCerebral microbleeds, NIHSS, and infarction size were identified as HT predictors. The best predicting models were RFC and GBC capable of capturing nonlinear interaction between predictors. Predictor interaction suggests a dynamic, rather than, fixed cutoff risk value for any of these predictors.
Source: Frontiers in Neurology - November 24, 2022 Category: Neurology Source Type: research

Influence of the Intensive Care Unit Environment on the Reliability of the Montreal Cognitive Assessment
Conclusions: The reliability of the MoCA was excellent, independent from the testing environment being ICU or office. This finding is helpful for patient care and studies investigating the effect of therapeutic interventions on the neuropsychological outcome after SAH, stroke or traumatic brain injury.
Source: Frontiers in Neurology - July 2, 2019 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

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

Computational Approaches for Acute Traumatic Brain Injury Image Recognition
In recent years, there have been major advances in deep learning algorithms for image recognition in traumatic brain injury (TBI). Interest in this area has increased due to the potential for greater objectivity, reduced interpretation times and, ultimately, higher accuracy. Triage algorithms that can re-order radiological reading queues have been developed, using classification to prioritize exams with suspected critical findings. Localization models move a step further to capture more granular information such as the location and, in some cases, size and subtype, of intracranial hematomas that could aid in neurosurgical ...
Source: Frontiers in Neurology - March 9, 2022 Category: Neurology Source Type: research

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