Latest AI and machine learning research in strokes for healthcare professionals.
Recent advances in digital technology are remarkable, and they are driving profound transformations in healthcare and medical research. Within this context, digital hypertension has emerged as a multidisciplinary paradigm that integrates novel digital technologies into the prevention, diagnosis, and management of hypertension. Digital hypertension encompasses diverse domains such as advanced senso...
BACKGROUND: Atrial fibrillation (AF) is a common and clinically heterogeneous arrhythmia. Machine learning algorithms can define data-driven disease subtypes in an unbiased fashion, but whether these AF subgroups align with underlying mechanisms, such as polygenic liability to AF or inflammation, and associate with clinical outcomes is unclear. METHODS: We identified individuals with AF in a large...
OBJECTIVE: The optimal treatment for distal medium vessel occlusion (DMVO) stroke remains uncertain, and evidence comparing endovascular therapy (EVT)...
BACKGROUND: Nearly half of the patients who received endovascular thrombectomy (EVT) for large vessel occlusion experience poor functional outcomes. R...
Stroke is a leading cause of long-term disability, often affecting upper-limb motor function and requiring continuous assessment. The Fugl-Meyer Asses...
Coronary atherosclerosis is a leading cause of morbidity and mortality worldwide and is characterized by complex molecular and cellular mechanisms inv...
Identifying reliable circulating biomarkers is crucial for improving the diagnosis and risk stratification of patients with ischemic stroke. In this s...
BACKGROUND: Recognizing knee hyperextension during gait in stroke patients is clinically challenging and involves cumbersome, costly procedures. This ...
PURPOSE: We aimed to identify key midlife dementia predictors and develop a novel machine learning (ML) -enabled risk prediction model. METHODS: Using...
BACKGROUND: Stroke leads to complex chronic structural and functional brain changes that specifically affect motor outcomes. The brain predicted age d...
INTRODUCTION: To assess the potential benefit of artificial intelligence (AI) based imaging software in supporting mechanical thrombectomy (MT) transf...
Pulmonary embolism (PE) remains a major diagnostic challenge due to its potentially life-threatening nature and the clinical burden associated with an...
OBJECTIVES: This study aimed to investigate the impacts of chronic diseases such as hypertension, dyslipidaemia and diabetes on personal and household...
Accurately predicting the prognosis of patients with acute ischemic stroke at discharge remains highly challenging after active treatment. The aim of ...
This study aims to construct a predictive model for post-thrombectomy hemorrhagic transformation (HT) by integrating hemodynamic features derived from...
BACKGROUND: The impact of discordance between remnant cholesterol (RC) and low-density lipoprotein cholesterol (LDL-c) on diabetes, diabetic kidney di...
INTRODUCTION: Stroke remains a leading cause of global morbidity and mortality, ranking second in deaths and third in disability-adjusted life years (...
INTRODUCTION: Stroke ranks as the second-leading cause of death and third in combined death and disability globally. In Ghana, there is a significant ...
Alzheimer's disease (AD) is a progressive neurodegenerative disorder that disrupts cognitive function across multiple domains, particularly affecting ...
In recent years, advances in imaging analysis technologies, including CT perfusion, MRI, and AI analysis, have extended the therapeutic time window fo...