Latest AI and machine learning research in strokes for healthcare professionals.
Atrial Fibrillation (AFib) is the most common sustained cardiac arrhythmia and is associated with substantial morbidity and mortality, including increased risk of stroke and heart failure. Accurate detection of AFib in long-term electrocardiographic (ECG) monitoring is essential for timely diagnosis and early clinical intervention, particularly in ambulatory settings using wearable devices. In thi...
Diabetes mellitus (DM) is an escalating global public health concern, with a rapidly increasing burden in low- and middle-income countries, including Bangladesh. Despite its growing prevalence and associated complications such as cardiovascular disease, kidney failure and stroke, comprehensive evidence on its determinants and predictive modeling at the population level remains limited. This study ...
Over the past 35 years, my work has focused on developing and studying robotic technologies to promote hand and arm recovery after stroke. In this Poi...
Although pharmacological thrombolysis and mechanical thrombectomy are standard treatments for thromboembolic diseases, they are limited by hemorrhagic...
ETHNOPHARMACOLOGICAL RELEVANCE: Taohong Siwu Decoction (TSD), originating from the Qing Dynasty medical text Gynecology Ice Mirror, is a representativ...
BACKGROUND: Warfarin dosing in cancer patients is challenging due to altered pathophysiology and variable responses. While machine learning offers pre...
OBJECTIVE: Rural hospital closures in the U.S. reduce access to essential healthcare services and worsen health and economic outcomes in rural communi...
Evaluating agreement between AI-generated functional assessment metrics and clinician judgment in stroke rehabilitation is important for understanding...
BACKGROUND: Diseases exist on spectra of risk factors, cellular perturbations, organ dysfunction, and clinical manifestations. It is unknown whether t...
The global prevalence of overweight and obesity is rising, and recent studies have established an independent contribution of adiposity to stroke risk...
PURPOSE: Glioblastoma (GBM) is the most prevalent and aggressive form of malignant glioma. Reliable estimation of progression-free survival (PFS) prio...
BACKGROUND: Artificial intelligence (AI) has rapidly emerged within healthcare systems and neurological rehabilitation with the potential to revolutio...
BACKGROUND: Warfarin dosing varies widely due to genetic, demographic, and clinical factors, but it is unknown whether the importance, equilibrium and...
OBJECTIVES: The pathophysiology of idiopathic intracranial hypertension (IIH) is poorly understood and disease-specific biomarkers are lacking. We aim...
Coronary no-reflow (NR) after percutaneous coronary intervention (PCI) predicts adverse prognosis in patients with acute coronary syndrome (ACS). This...
Educators would benefit from developing course material that increases understanding of generative artificial intelligence (AI). General-purpose large...
BACKGROUND: Environmental exposures are known contributors to chronic disease but are rarely incorporated into risk prediction models. OBJECTIVE: We d...
Accurately predicting long-term outcomes after stroke remains a key challenge in personalized medicine. Here, we present a neuroimaging platform that ...
BackgroundPredicting post-stroke cognitive impairment (PSCI) remains challenging.ObjectiveThis study validated two brain age metrics-Gray Matter Brain...
BACKGROUND: Limb-girdle muscular dystrophy R2-dysferlin related (LGMD-R2) is a progressive muscle condition with marked variability in disease course,...