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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

Predictive model, miRNA-TF network, related subgroup identification and drug prediction of ischemic stroke complicated with mental disorders based on genes related to gut microbiome
ConclusionThrough comprehensive analysis, a diagnostic prediction model with good effect was obtained. Both the training group (AUC 0.82, CI 0.93–0.71) and the verification group (AUC 0.81, CI 0.90–0.72) had a good phenotype in the qRT-PCR test. And in verification group 2 we validated between the two groups with and without carotid-related ischemic cerebrovascular events (AUC 0.87, CI 1–0.64). MiRNA (hsa-mir-129-2-3p, has-mir-335-5p, and has-mir-16-5p) and TFs (CREB1, FOXL1), which may be related to IS, were obtained.
Source: Frontiers in Neurology - May 26, 2023 Category: Neurology Source Type: research

CT-based thrombus radiomics nomogram for predicting secondary embolization during mechanical thrombectomy for large vessel occlusion
ConclusionThis nomogram could be used to optimize the surgical MT procedure for LVO based on the risk of developing SE.
Source: Frontiers in Neurology - May 12, 2023 Category: Neurology Source Type: research

Rehabilomics: A state-of-the-art review of framework, application, and future considerations
Rehabilomics is an important research framework that allows omics research built upon rehabilitation practice, especially in function evaluation, outcome prediction, and individualized rehabilitation. In the field of rehabilomics, biomarkers can serve as objectively measured indicators for body functioning, so as to complement the International Classification of Functioning, Disability, and Health (ICF) assessment. Studies on traumatic brain injury (TBI), stroke, and Parkinson's disease have shown that biomarkers (such as serum markers, MRI, and digital signals derived from sensors) are correlated with diagnosis, disease s...
Source: Frontiers in Neurology - March 8, 2023 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

Machine learning segmentation of core and penumbra from acute stroke CT perfusion data
We present an alternative model for the estimation of tissue fate using multiple perfusion measures simultaneously.MethodsWe used machine learning (ML) models based on four different algorithms, combining four CTP measures (cerebral blood flow, cerebral blood volume, mean transit time and delay time) plus 3D-neighborhood (patch) analysis to predict the acute ischemic core and perfusion lesion volumes. The model was developed using 86 patient images, and then tested further on 22 images.ResultsXGBoost was the highest-performing algorithm. With standard threshold-based core and penumbra measures as the reference, the model d...
Source: Frontiers in Neurology - February 23, 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-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

A deep learning approach to predict collateral flow in stroke patients using radiomic features from perfusion images
We present a multi-stage deep learning approach to predict collateral flow grading in stroke patients based on radiomic features extracted from MR perfusion data. First, we formulate a region of interest detection task as a reinforcement learning problem and train a deep learning network to automatically detect the occluded region within the 3D MR perfusion volumes. Second, we extract radiomic features from the obtained region of interest through local image descriptors and denoising auto-encoders. Finally, we apply a convolutional neural network and other machine learning classifiers to the extracted radiomic features to ...
Source: Frontiers in Neurology - February 21, 2023 Category: Neurology Source Type: research

Stroke risk prediction by color Doppler ultrasound of carotid artery-based deep learning using Inception V3 and VGG-16
ConclusionIn this research, we classified color Doppler ultrasound images into high-risk carotid vulnerable and stable carotid plaques. We fine-tuned pre-trained deep learning models to classify color Doppler ultrasound images according to our dataset. Our suggested framework helps prevent incorrect diagnoses caused by low image quality and individual experience, among other factors.
Source: Frontiers in Neurology - February 14, 2023 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

Image-to-image generative adversarial networks for synthesizing perfusion parameter maps from DSC-MR images in cerebrovascular disease
Stroke is a major cause of death or disability. As imaging-based patient stratification improves acute stroke therapy, dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) is of major interest in image brain perfusion. However, expert-level perfusion maps require a manual or semi-manual post-processing by a medical expert making the procedure time-consuming and less-standardized. Modern machine learning methods such as generative adversarial networks (GANs) have the potential to automate the perfusion map generation on an expert level without manual validation. We propose a modified pix2pix GAN with a tempo...
Source: Frontiers in Neurology - January 10, 2023 Category: Neurology Source Type: research

Comparison of ischemic stroke diagnosis models based on machine learning
ConclusionThe LASSO, SVM-RFE, and RF models have good prediction abilities. However, the ANN model is efficient at classifying positive samples and is unsuitable at classifying negative samples.
Source: Frontiers in Neurology - December 5, 2022 Category: Neurology Source Type: research

Memory decline in young stroke survivors during a 9-year follow-up: A cohort study
ConclusionYoung stroke survivors might be at risk of memory decline over the decade following the stroke.
Source: Frontiers in Neurology - November 25, 2022 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

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