Strokes

Prediction of stroke-associated hospital-acquired pneumonia: Machine learning approach.

BACKGROUND: Stroke-associated Hospital Acquired Pneumonia (HAP) significantly impacts patient outcomes. This study explores the utility of machine learning models in predicting HAP in stroke patients, leveraging national registry data and SHapley Additive exPlanations (SHAP) analysis to identify key predictive factors.

Machine learning and deep learning algorithms in stroke medicine: a systematic review of hemorrhagic transformation prediction models.

BACKGROUND: Acute ischemic stroke (AIS) is a major cause of morbidity and mortality, with hemorrhagic transformation (HT) further worsening outcomes. Traditional scoring systems have limited predictive accuracy for HT in AIS. Recent research has explored machine learning (ML) and deep learning (DL) algorithms for stroke management. This study evaluates and compares the effectiveness of ML and DL a...

Residual risk prediction in anticoagulated patients with atrial fibrillation using machine learning: A report from the GLORIA-AF registry phase II/III.

BACKGROUND: Although oral anticoagulation decreases the risk of thromboembolism in patients with atrial fibrillation (AF), a residual risk of thrombot...

Subtyping strokes using blood-based protein biomarkers: A high-throughput proteomics and machine learning approach.

BACKGROUND: Rapid diagnosis of stroke and its subtypes is critical in early stages. We aimed to discover and validate blood-based protein biomarkers t...

Integrating AI-driven wearable devices and biometric data into stroke risk assessment: A review of opportunities and challenges.

Stroke is a leading cause of morbidity and mortality worldwide, and early detection of risk factors is critical for prevention and improved outcomes. ...

End-to-end deep learning patient level classification of affected territory of ischemic stroke patients in DW-MRI.

PURPOSE: To develop an end-to-end DL model for automated classification of affected territory in DWI of stroke patients.

Automated Segmentation of MRI White Matter Hyperintensities in 8421 Patients with Acute Ischemic Stroke.

BACKGROUND AND PURPOSE: To date, only a few small studies have attempted deep learning-based automatic segmentation of white matter hyperintensity (WM...

Influence of renal function on blood pressure control and outcome in thrombolyzed patients after acute ischemic stroke: analysis of the ENCHANTED trial.

BACKGROUND: The effect of renal impairment in patients who receive intravenous thrombolysis for acute ischemic stroke (AIS) is unclear. We aimed to de...

Advancing personalised care in atrial fibrillation and stroke: The potential impact of AI from prevention to rehabilitation.

Atrial fibrillation (AF) is a complex condition caused by various underlying pathophysiological disorders and is the most common heart arrhythmia worl...

Serum glial fibrillary acidic protein in acute stroke: feasibility to determine stroke-type, timeline and tissue-impact.

BACKGROUND: Interest is emerging regarding the role of blood biomarkers in acute stroke. The aim of this pilot study was to determine the feasibility ...

Design and validation of Withings ECG Software 2, a tiny neural network based algorithm for detection of atrial fibrillation.

BACKGROUND: Atrial Fibrillation (AF) is the most common form of arrhythmia in the world with a prevalence of 1%-2%. AF is also associated with an incr...

Effectiveness of unilateral lower-limb exoskeleton robot on balance and gait recovery and neuroplasticity in patients with subacute stroke: a randomized controlled trial.

BACKGROUND: Impaired balance and gait in stroke survivors are associated with decreased functional independence. This study aimed to evaluate the effe...

Prediction of prolonged mechanical ventilation in the intensive care unit via machine learning: a COVID-19 perspective.

Early recognition of risk factors for prolonged mechanical ventilation (PMV) could allow for early clinical interventions, prevention of secondary com...

Machine learning evaluation of a hypertension screening program in a university workforce over five years.

The global prevalence of hypertension continues excessively elevated, especially among low- and middle-income nations. Workplaces provide tremendous o...

Deep learning-based segmentation of acute ischemic stroke MRI lesions and recurrence prediction within 1 year after discharge: A multicenter study.

OBJECTIVE: To explore the performance of deep learning-based segmentation of infarcted lesions in the brain magnetic resonance imaging (MRI) of patien...

Pathological Asymmetry-Guided Progressive Learning for Acute Ischemic Stroke Infarct Segmentation.

Quantitative infarct estimation is crucial for diagnosis, treatment and prognosis in acute ischemic stroke (AIS) patients. As the early changes of isc...

Identification of an immune-related gene panel for the diagnosis of pulmonary arterial hypertension using bioinformatics and machine learning.

OBJECTIVE: This study aimed to screen an immune-related gene (IRG) panel and develop a novel approach for diagnosing pulmonary arterial hypertension (...

Machine learning-based diagnostic model for stroke in non-neurological intensive care unit patients with acute neurological manifestations.

Stroke is a neurological complication that can occur in patients admitted to the intensive care unit (ICU) for non-neurological conditions, leading to...

Deep Learning Based Detection of Large Vessel Occlusions in Acute Ischemic Stroke Using High-Resolution Photon Counting Computed Tomography and Conventional Multidetector Computed Tomography.

PURPOSE: Deep learning (DL) methods for detecting large vessel occlusion (LVO) in acute ischemic stroke (AIS) show promise, but the effect of computed...

PhenoFlow: A Human-LLM Driven Visual Analytics System for Exploring Large and Complex Stroke Datasets.

Acute stroke demands prompt diagnosis and treatment to achieve optimal patient outcomes. However, the intricate and irregular nature of clinical data ...