Latest AI and machine learning research in hypertension for healthcare professionals.
Artificial intelligence (AI)-enhanced electrocardiogram (ECG) models are designed to detect specific anatomical and functional cardiac abnormalities. Understanding the selectivity of their phenotypic associations is essential to inform their clinical use. Here, we sought to assess whether AI-ECG models function as condition-specific classifiers or broader cardiovascular risk markers. We included f...
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...
Machine learning models for predicting structural heart disease (SHD) from electrocardiography (ECG) traditionally required structured echocardiograph...
Artificial intelligence applied to electrocardiography (AI-ECG) can derive a heart age or ECG-age, potentially reflecting waveform patterns that indic...
Bronchopulmonary dysplasia (BPD) and pulmonary hypertension (PH) are leading causes of morbidity and mortality in premature infants. To determine whet...
Sudden cardiac death (SCD) in young individuals—especially athletes—remains difficult to diagnose due to overlapping physiological and pathological el...
Hypertension is a major modifiable risk factor for cardiovascular diseases. Early identification of high-risk individuals using predictive models can ...
Diabetes, hypertension, and dyslipidemia are major risk factors for cardiovascular (CVD), cerebral, and renal diseases (RD). However, the underlying m...
Hypertension (HTN) is a leading, yet often underdiagnosed, cause of cardiovascular diseases worldwide. While clinical risk scores like the Framingham ...
Hypertensive heart disease (HHD) encompasses diverse clinical profiles, comorbidities, and cardiac remodeling, but current classifications insufficien...
Alzheimer’s Disease and Related Dementias (ADRD) affect millions worldwide and can begin over a decade before symptoms appear. ADRD are generally irre...
Objective assessment of left ventricular function remains a key prognosticator that is used to guide therapeutic decisions for patients with heart fai...
Artificial intelligence (AI) applied to routine electrocardiograms (ECGs) offers promise for screening of structural heart disease (SHD), yet broad cl...
Cardiovascular Diseases (CVDs) remain the leading cause of death worldwide, creating an urgent need for accurate risk prediction. Machine learning (ML...
Pulmonary hypertension (PH) is a highly morbid disease, but underdiagnosis is common outside of expert referral centers. Consequentially, there may be...
Control of blood pressure (BP) continues to be a challenge globally. Clinical trials have shown home BP monitoring and text-message interventions to l...
Diastolic dysfunction is a precursor to heart failure with preserved ejection fraction (HFpEF), and early detection by electrocardiography (ECG) would...
Frailty and multimorbidity are common in older adults and contribute substantially to prolonged hospitalizations, readmissions, and mortality. Yet, ex...
Point-of-care ultrasonography (POCUS) enables clinicians to obtain critical diagnostic information at the bedside especially in resource limited setti...
Sleep disorders, including insomnia and obstructive sleep apnea, affect millions of individuals worldwide but are frequently undetected due to the hig...