Strokes

Factors Associated with Machine Learning-Based Predictions of Retinal Aging Using Teleretinal Screening Images from Patients with Diabetes.

PURPOSE: To identify factors associated with accelerated retinal aging based on machine learning predictions of age using fundus images from teleretinal screening of patients with diabetes. DESIGN: Cross-sectional study of retinal images. SUBJECTS: Ten thousand, five hundred thirty eye images from 2939 patients with diabetes who underwent teleretinal screening at the University of California clini...

Artificial Intelligence-assisted Diagnosis of Carotid Artery Calcifications on Panoramic Radiographs: A Meta-analysis.

Carotid artery calcifications (CACs), a known risk factor for stroke, can be detected on panoramic radiographs (PRs). However, this clinically significant pathophysiological condition has long been underdiagnosed due to insufficient training and expertise among dentists. Artificial intelligence (AI) may serve as a valuable tool to aid dentists in detecting CACs on PRs. This meta-analysis was condu...

TPCNet: A Temporal Periodicity Convolutional Network for motor imagery EEG decoding in stroke patients.

BACKGROUND: Stroke caused by vascular rupture or blockage has high incidence and leads to significant disability. Motor imagery (MI) electroencephalog...

Self-induced large pitch artificial muscles with giant stroke for soft robotic applications.

Achieving large initial coil pitches and contractile strokes in twisted and coiled polymer artificial muscles often requires complex and multi-step fa...

Developing and validating a machine learning model for predicting post-thrombolysis seizures in acute ischemic stroke.

Post-stroke seizures (PSS) manifests variably due to ischemic brain injury, yet its risk factors remain unclear. This study developed a machine learni...

A scoping review of portable ultra-low-field MRI studies in patients with acquired brain injury: Past, present, and future.

Acquired Brain Injury (ABI) refers to any post-birth damage to the brain, commonly resulting from traumatic events (traumatic brain injury) or non-tra...

Validation of socialbit as a smartwatch algorithm for social interaction detection in a clinical population.

Social interaction supports brain health and recovery after neurological injury. Yet no validated tool exists for real-time measurement in individuals...

A hybrid swin transformer-BiLSTM framework and ensemble learning for multimodal brain stroke detection and risk prediction.

Stroke is one of the leading causes of mortality and long-term disability worldwide, primarily resulting from the sudden disruption of cerebral blood ...

Prediction of Fontan failure and correlates of Fontan-associated liver disease severity using machine learning and radiomic features from multi-parametric abdominal MRI.

BACKGROUND: Fontan-associated liver disease (FALD) is associated with morbidity and mortality in patients with palliated single ventricle congenital h...

A rapid and accurate guanidine CEST imaging in ischemic stroke using a machine learning approach.

OBJECTIVE: Rapid and accurate mapping of brain tissue pH is crucial for early diagnosis and management of ischemic stroke. Amide proton transfer (APT)...

Machine Learning Models for Predicting Stroke-Associated Pneumonia: A Systematic Review and Meta-Analysis.

Stroke-associated pneumonia (SAP) is a frequent and severe complication following stroke. Recently, several machine learning (ML) models have been dev...

Prediction of recurrent ischemic stroke using machine learning from real-world data.

BACKGROUND: Recurrent ischemic stroke (RIS) is a significant challenge in Malaysia, affecting approximately 33% of patients. However, studies using ar...

Enhancing upper limb motor recovery prediction after acute stroke using EEG and subacute data.

Electroencephalography (EEG) has shown promise in assessing and monitoring functional recovery in stroke survivors, but its utility in predicting uppe...

MEF2A-mediated inhibition of ischemic stroke injury via the PI3K/AKT pathway: a comprehensive bioinformatics and in vitro study.

Explainable multi-modal approach for uncovering key predictors of stroke-risk from ECG, EMG, blood pressure, and respiratory signals.

Accurate and timely stroke-risk prediction is necessary to help patients at risk take guided measures, as stroke remains a leading cause of death and ...

Graph Convolutional Networks Enable Fast Hemorrhagic Stroke Monitoring With Electrical Impedance Tomography.

OBJECTIVE: To develop a fast image reconstruction method for stroke monitoring with electrical impedance tomography with image quality comparable to c...

Prediction of mortality, bleeding, and ischaemic events in patients with cancer and acute coronary syndrome: a model development and validation study.

BACKGROUND: Accurate assessment of mortality, bleeding, and atherothrombotic risk in patients with cancer and acute coronary syndrome could inform nov...

The Impact of a Neuroradiologist on the Report of a Real-World CT Perfusion Imaging Map Derived from Artificial Intelligence/Machine Learning-Driven Software.

BACKGROUND AND PURPOSE: According to the guideline, CT perfusion should be read and analyzed by using computer-aided software. This study evaluates th...

Integrative transcriptomics, machine learning, and molecular docking reveal that nicotine exacerbates ischemic stroke: TLR4-dependent activation of NLRP3/caspase-1/GSDMD-mediated microglial pyroptosis.

OBJECTIVE: This study aimed to clarify the molecular mechanisms through which nicotine (Nic) aggravates ischemic stroke (IS), with an emphasis on infl...

Machine learning models for predicting futile recanalisation after endovascular treatment in patients with large core infarction.

BACKGROUND: Predicting futile recanalisation following endovascular treatment (EVT) in patients with large core infarctions is crucial for guiding cli...