AIMC Topic: Ischemic Stroke

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Deep learning to identify stroke within 4.5 h using DWI and FLAIR in a prospective multicenter study.

Scientific reports
To enhance thrombolysis eligibility in acute ischemic stroke, we developed a deep learning model to estimate stroke onset within 4.5 h using diffusion-weighted imaging (DWI) and fluid-attenuated inversion recovery (FLAIR) images. Given the variabilit...

Analyses of the mechanism and therapeutic targets of senescence related genes in ischemic stroke with multi-omics approach.

Scientific reports
Ischemic stroke (IS) affects 11 million people annually, posing substantial clinical and economic burdens. Current therapies remain limited by time sensitivity and variable efficacy, necessitating novel biomarkers. We developed a multi-omics framewor...

A machine learning model reveals invisible microscopic variation in acute ischaemic stroke (≤ 6 h) with non-contrast computed tomography.

BMC medical imaging
BACKGROUND: In most medical centers, particularly in primary hospitals, non-contrast computed tomography (NCCT) serves as the primary imaging modality for diagnosing acute ischemic stroke. However, due to the small density difference between the infa...

Machine learning-based prognostic prediction for acute ischemic stroke using whole-brain and infarct multi-PLD ASL radiomics.

BMC medical imaging
INTRODUCTION: Accurate early prognostic prediction for acute ischemic stroke (AIS) is essential for guiding personalized treatment. This study aimed to assess the predictive value of radiomics features from whole-brain and infarct cerebral blood flow...

Metabolomic biomarkers could be molecular clocks in timing stroke onset.

Scientific reports
The preferred treatment for acute ischaemic stroke (AIS) is intravenous thrombolysis (IVT) administered within 4.5 hours (h) of symptom onset. This study aimed to identify metabolomic biomarkers for distinguishing AIS patients within 4.5 h of symptom...

A two-stage machine learning-based risk assessment model for intravenous thrombolysis in acute ischemic stroke (AIS): A multi-center modeling study of pooled datasets.

International journal of medical informatics
OBJECTIVE: Develop a two-stage, machine learning-based thrombolysis risk stratification model from existing medical datasets and electronic health records to predict the risk of early hemorrhagic transformation(HT) and in-hospital mortality(IM) follo...

Triclosan exposure potentiates ischemic stroke risk: Multi-omics integration and molecular docking unveil neurotoxic mechanisms.

Ecotoxicology and environmental safety
This study applied network toxicology and multimodal biological approaches integrated with machine learning to systematically identify four TCS-IS-related genes, providing a comprehensive understanding of the pathophysiological relationship between t...

Multimodal deep learning for predicting unsuccessful recanalization in refractory large vessel occlusion.

European journal of radiology
PURPOSE: This study explores a multi-modal deep learning approach that integrates pre-intervention neuroimaging and clinical data to predict endovascular therapy (EVT) outcomes in acute ischemic stroke patients. To this end, consecutive stroke patien...

Automated Vessel Occlusion Software in Acute Ischemic Stroke: Pearls and Pitfalls.

Stroke
Software programs leveraging artificial intelligence to detect vessel occlusions are now widely available to aid in stroke triage. Given their proprietary use, there is a surprising lack of information regarding how the software works, who is using t...

Comparison of lesion segmentation performance in diffusion-weighted imaging and apparent diffusion coefficient images of stroke by artificial neural networks.

PloS one
Stroke is the second leading cause of death, accounting for 11% of deaths worldwide. Comparing diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) images is important for stroke diagnosis, but most studies have focused on lesion...