Strokes

Metabolic Subphenotypes of Obstructive Sleep Apnea: NHANES 2017-2020 (pre-pandemic)

OSA and MetS have a bidirectional relationship but increasing evidence suggests metabolic heterogeneity in OSA, systematic phenotyping of metabolic drivers in OSA are lack. To identify metabolic subphenotypes of OSA and elucidate potential pathophysiological mechanisms using population-level data. To analyze the data related to OSA and MetS from 2,260 participants in the NHANES database (2017–2020...

Independent contributions of language activations in left and right temporal cortex to aphasia outcomes after stroke

Recovery from aphasia after stroke is thought to depend on functional reorganization of language processing in surviving brain regions. Many studies have investigated this process, but progress has been impeded by methodological limitations relating to task performance confounds, contrast validity, and sample sizes. Furthermore, few studies have accounted for the complex relationships that exist b...

Evaluating Accuracy and Reasoning Capabilities of Large Language Models for Acute Ischemic Stroke Management

Acute ischemic stroke (AIS) management has evolved substantially over the past two decades, with mechanical thrombectomy adding complexity that requir...

Prediction of recurrence and functional status in young ischemic stroke patients: Comparison of machine learning and traditional statistical methods

Ischemic stroke in young adults is a significant social and economic burden. Machine learning (ML) techniques can potentially predict the outcomes of ...

Predicting Acute Cerebrovascular Events in Stroke Alerts Using Large-Language Models and Structured Data

Acute stroke alerts are often activated for non-cerebrovascular conditions, leading to false positives that strain clinical resources and promote diag...

Deep learning-based prediction of cardiopulmonary disease in retinal images of premature infants

Bronchopulmonary dysplasia (BPD) and pulmonary hypertension (PH) are leading causes of morbidity and mortality in premature infants. To determine whet...

Automated Deep Learning-Based Detection of Early Atherosclerotic Plaques in Carotid Ultrasound Imaging

Carotid plaque presence is associated with cardiovascular risk, even among asymptomatic individuals. While deep learning has shown promise for carotid...

RAX-NET: Residual Attention Xception Network for Brain Ischemic Stroke Segmentation in T1-Weighted MRI

Ischemic stroke, caused by arterial occlusion, leads to hypoxia and cellular necrosis. Rapid and accurate delineation of ischemic lesions is essential...

A systematic review of early neuroimaging and neurophysiological biomarkers for post-stroke mobility prognostication

Accurate prognostication of mobility outcomes is essential to guide rehabilitation and manage patient expectations. The prognostic utility of neuroima...

Development of a Hypertension Risk Prediction Model using Nationally Representative Survey Data: A Machine Learning Approach and Web Application Deployment

Hypertension is a major modifiable risk factor for cardiovascular diseases. Early identification of high-risk individuals using predictive models can ...

Plasma Proteomics Linking Primary and Secondary diseases: Insights into Molecular Mediation from UK Biobank Data

Diabetes, hypertension, and dyslipidemia are major risk factors for cardiovascular (CVD), cerebral, and renal diseases (RD). However, the underlying m...

Development of a RAG-based Expert LLM for Clinical Support in Radiation Oncology

The ability of pre-trained large language models (LLMs) to rapidly master novel natural language processing tasks holds transformative potential. Howe...

Incidentally discovered Covert Cerebrovascular Disease by CT versus MRI: Agreement and Prognostic Value for Stroke and Dementia in a Large Real-World Cohort

Covert cerebrovascular disease (CCD), comprising covert brain infarction (CBI) and white matter disease (WMD), is common in older adults and linked to...

Comprehensive, Transparent, and Fair Machine Learning Models for Hypertension Risk Prediction: Benchmarking With Framingham, External Validation, Individual-Level Analysis, and Equitable Clinical Utility

Hypertension (HTN) is a leading, yet often underdiagnosed, cause of cardiovascular diseases worldwide. While clinical risk scores like the Framingham ...

Evaluation of Care Quality for Atrial Fibrillation Across Non-Interoperable Electronic Health Record Data using a Retrieval-Augmented Generation-enabled Large Language Model

Standardized assessment of clinical quality measures from electronic health records (EHRs) is challenging because information is fragmented across str...

Clustered Phenotypes of Hypertensive Heart Disease With Strain Measurements Reveals Distinct Characteristics, Clinical Course, and Prognosis

Hypertensive heart disease (HHD) encompasses diverse clinical profiles, comorbidities, and cardiac remodeling, but current classifications insufficien...

Two-Stage Machine Learning Based Prediction of Thrombophilia Management

Thrombophilia diagnosis and management rely on the nuanced interpretation of clinical history, risk factors, and laboratory data, yet significant vari...

Long-Term Carotid Plaque Progression and the Role of Intraplaque Hemorrhage: A Deep Learning-Based Analysis of Longitudinal Vessel Wall Imaging

Carotid atherosclerosis is a major contributor in the etiology of ischemic stroke. Although intraplaque hemorrhage (IPH) is known to increase stroke r...

Proteomic signatures and machine learning based-prediction models for cardiovascular risk in survivors of myocardial infarction

Survivors of myocardial infarction (MI) are still at risk for adverse long-term outcomes such as all-cause mortality, heart failure (HF), and ischemic...

Finding the Goldilocks zone for toddler accelerometry: how many days are needed for a reliable estimate of physical activity using machine learning?

Accelerometers are used to measure sedentary time (SED) and physical activity (PA) in toddlers, but they may struggle to wear them for extended period...