Cardiovascular

Strokes

Latest AI and machine learning research in strokes for healthcare professionals.

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A wrapper method for finding optimal subset of multimodal Magnetic Resonance Imaging sequences for ischemic stroke lesion segmentation.

Multimodal data, while being information-rich, contains complementary as well as redundant informati...

Screening prediction models using artificial intelligence for moderate-to-severe obstructive sleep apnea in patients with acute ischemic stroke.

BACKGROUND: Obstructive sleep apnea (OSA) is common after stroke. Still, routine screening of OSA wi...

Identifying the presence of atrial fibrillation during sinus rhythm using a dual-input mixed neural network with ECG coloring technology.

BACKGROUND: Undetected atrial fibrillation (AF) poses a significant risk of stroke and cardiovascula...

Optimizing hypertension prediction using ensemble learning approaches.

Hypertension (HTN) prediction is critical for effective preventive healthcare strategies. This study...

Using clinical data to reclassify ESUS patients to large artery atherosclerotic or cardioembolic stroke mechanisms.

PURPOSE: Embolic stroke of unidentified source (ESUS) represents 10-25% of all ischemic strokes. Our...

Predictive modeling of ICU-AW inflammatory factors based on machine learning.

BACKGROUND: ICU-acquired weakness (ICU-AW) is a common complication among ICU patients. We used mach...

Machine Learning-Based Model for Prediction of Post-Stroke Cognitive Impairment in Acute Ischemic Stroke: A Cross-Sectional Study.

BACKGROUND AND OBJECTIVE: Early identification of post-stroke cognitive impairment (PSCI) is an impo...

ECG-based machine learning model for AF identification in patients with first ischemic stroke.

BACKGROUND: The recurrence rate of strokes associated with atrial fibrillation (AF) can be substanti...

Prediction of postoperative stroke in patients experienced coronary artery bypass grafting surgery: a machine learning approach.

BACKGROUND: Coronary artery bypass grafting (CABG) surgery has been a widely accepted method for tre...

Artificial intelligence in emergency neuroradiology: Current applications and perspectives.

Emergency neuroradiology provides rapid diagnostic decision-making and guidance for management for a...

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

BACKGROUND: Stroke-associated Hospital Acquired Pneumonia (HAP) significantly impacts patient outcom...

Smartphone pupillometry with machine learning differentiates ischemic from hemorrhagic stroke: A pilot study.

OBJECTIVES: Similarities between acute ischemic and hemorrhagic stroke make diagnosis and triage cha...

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 hemorrhagi...

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 disc...

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 ...

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