Cardiovascular

Strokes

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

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Neuromechanical Predictors of Clinical Scores of Balance and Functional Mobility in Chronic Stroke Survivors – A Machine Learning Approach

Clinical tests such as the Berg Balance Scale (BBS) and Timed Up and Go (TUG) are used to assess balance and functional mobility following stroke. These tests are subjective due to dependence on the assessor’s judgment and the patient’s effort, potentially affecting their sensitivity to early or subtle balance recovery. This study aimed to identify objective markers of BBS and TUG among corticomus...

Combination AI-Machine Learning to Diagnose Pulmonary Hypertension: A Real-World Evidence Cohort Study

Pulmonary hypertension (PH) is a highly morbid disease, but underdiagnosis is common outside of expert referral centers. Consequentially, there may be opportunities to automate PH diagnosis using artificial intelligence (AI) clinical decision support tools. Analysis of patient-level right heart catheterization (RHC) data is required to optimize AI-based PH diagnosis but has not been reported previ...

From Concept to Code: AI- Powered CODE-ICH Transforming Acute Neurocritical Response for Hemorrhagic Strokes

Intracerebral hemorrhage (ICH) is among the most devastating forms of stroke, characterized by high early mortality and limited time-sensitive treatme...

Machine Learning Risk Prediction for Prolonged Hospitalization in Frail Older Adults with Multimorbidity

Frailty and multimorbidity are common in older adults and contribute substantially to prolonged hospitalizations, readmissions, and mortality. Yet, ex...

Deep Learning Prediction of Left Atrial Structure and Function from 12-lead Electrocardiograms

Abnormal cardiac atrial structure and function (atrial cardiopathy)1 typically precedes atrial fibrillation (AF) and predicts other cardiovascular com...

Agentic Generative Artificial Intelligence System for Classification of Pathology-Confirmed Primary Progressive Aphasia Variants

Accurate clinical and pathological diagnoses are essential in neurodegenerative diseases, especially given the emergence of pathology-specific disease...

Modeling nonlinear and interaction effects of spatiotemporal and other non-genetic factors improves phenotypic prediction for complex traits

Adjusting for non-genetic factors can improve genetic association testing and polygenic prediction, yet most studies rely on linear adjustments for a ...

A photoplethysmography-based aging clock reveals genetic determinants of arterial aging

Arterial aging, marked by progressive vascular stiffening, is a contributor to cardiovascular disease. Photoplethysmography (PPG) waveforms offer an e...

RAGCBPNet: An Efficient Feature Fusion Framework for Wearable Cuffless Blood Pressure Monitoring and Long-term Validation in Real-world Settings

Wearable and cuffless blood pressure (BP) monitoring hold great promise for preventive hypertension management, yet few studies have been validated un...

Circulating microglia-derived extracellular vesicles predict recovery after rehabilitation in stroke survivors

Timely intensive rehabilitation is crucial to contrast the negative escalation of events that follow a stroke injury, to promote tissue regeneration, ...

Deep learning based ischemic lesion markers on non-contrast head CT compared to CTP and DWI

Quantification of ischemic brain tissue on non-contrast CT (NCCT) in acute ischemic stroke is challenging in the acute setting. To compare the spatial...

Ambient Only vs. Longitudinal Data-Enhanced AI Documentation: A Pilot Study Quantifying the Value of Historical Clinical Context in Primary Care

Ambient artificial intelligence (AI) clinical documentation tools have gained rapid adoption in healthcare to address physician burnout from documenta...

Visionary AI: Decoding Systemic Vascular Health and Hypertensive Disorders in Pregnancy Through Retinal Imaging and Artificial Intelligence

Pregnancy orchestrates a rare physiological transformation across vascular, immune, and metabolic systems. When this dynamic balance is disrupted – as...

Assessment of fatal cardiovascular disease risk using data-driven diabetes subgroups and SCORE2-Diabetes in 24,943 adults in Mexico City

Cardiovascular disease (CVD) is a leading cause of diabetes-related mortality in Mexico. Although diabetes subgroups capture underlying disease hetero...

Genetic and Etiological Insights from Automated Lumen Diameter Measurements in Carotid Ultrasounds of the UK Biobank

Carotid ultrasound is routinely used in clinical practice for non-invasive vascular anatomical and functional assessment. In particular, the carotid i...

GenBrain: A Generative Foundation Model of Multimodal Brain Imaging

Neuroimaging faces a reproducibility crisis, where studies on small, heterogeneous datasets produce unreliable brain-wide associations and AI models t...

Machine Learning Prediction of Blood Pressure Control in Patients With Hypertension and Heart Failure Using Longitudinal Clinical Data

To develop and validate machine learning models for predicting Blood Pressure (BP) control status using demographic characteristics and longitudinal B...

Dynamic Stroke Risk Stratification via Machine Learning: A Multi-Level Single-Center Study

Stroke is a leading global public health challenge and the second leading cause of death worldwide. In China, its burden continues to escalate amid po...

Opportunistically Detecting Signs of Hypertension on a Consumer Smartwatch

Hypertension is a silent killer, with over half of affected adults unaware of their condition1,2. This lack of awareness is a major concern, as early ...

A proteogenomic atlas of idiopathic pulmonary arterial hypertension reveals sex-dimorphic mechanisms and potential novel therapeutic targets

Current circulating biomarkers for idiopathic pulmonary arterial hypertension (IPAH) lack specificity for preclinical detection and fail to capture th...

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