Hypertension

Artificial intelligence-based prediction of transthoracic echocardiographic image quality in ICD/CRT-D candidates: A proof-of-concept study.

BACKGROUND: High-quality echocardiography is essential for accurate and reproducible assessment of cardiac functional indices, which are highly dependent on adequate image quality and proper probe alignment. An artificial intelligence (AI)-based approach may enable automated image quality assessment. AIMS: Our aim was to develop and internally validate an AI-based algorithm to predict image qualit...

Dynamic Beat-to-Beat Blood Pressure Estimation using a Multi-modal Wearable Deep Learning Approach.

Cuffless blood pressure (BP) monitoring technologies, primarily based on pulse transit time (PTT) or photoplethysmography (PPG), frequently suffer from calibration drift due to their reliance on blood volume surrogates rather than direct pressure measurement. To address this physiological limitation, this study presents a multi-modal deep learning framework that integrates superficial temporal art...

Dual branch fundus deep learning network as an enhanced multi classification system for ocular disease detection via hybrid feature fusion.

Ophthalmic diagnosis relies heavily on the interpretation of fundus images to identify a range of debilitating diseases. However, the presence of mult...

Opportunistic screening for incident cardiometabolic disease in metabolically healthy non-obese individuals: a prospective cohort study.

BACKGROUND: Metabolically healthy non-obese (MHN) individuals are considered at low cardiometabolic risk, yet a subset may harbor unfavorable visceral...

From Cardioprotection to Trial Design: Rethinking Cardiac Safety in Oncology.

PURPOSE OF REVIEW: Cardio-oncology has emerged as a pivotal discipline aimed at preserving cardiovascular health in patients undergoing contemporary c...

Clinical Evaluation of A-LIKNet: Deep Learning-accelerated Single-breath-hold CINE Magnetic Resonance Imaging for Cardiac Function Assessment.

OBJECTIVES: To evaluate the diagnostic accuracy and quantitative agreement of A-LIKNet (attention-incorporated network for sharing low-rank, image, an...

Multi-Center Adversarial Bi-Phase Cross-Attention Network for Right Ventricular Segmentation and Functional Classification in Echocardiography.

BACKGROUND: Automated right ventricular (RV) analysis in 2D echocardiography is limited by the morphological complexity of RV segmentation and the dom...

Time-dependent predictive contribution of the triglyceride-glucose index for incident cardiovascular disease in a 9-year Chinese cohort.

This study aims to investigate the association between triglyceride-glucose (TyG) index and risk of CVD using explainable survival analysis method bas...

Comparative machine learning approaches to prognosticate clinical outcomes in oral and maxillofacial space infections: a retrospective analysis.

BACKGROUND: The objective of this study was to evaluate the performance of multiple machine learning algorithms to provide evidence supporting early i...

Prognostic models for diabetic kidney disease outcomes: A systematic review.

OBJECTIVE: This systematic review aimed to systematically evaluate the methodological quality and predictive performance of existing prognostic models...

Statin adherence among patients with hypertension in Swedish primary health care: a QregPV cohort study using explainable machine learning.

AIMS: Evidence regarding statin adherence among patients with hypertension in primary care is limited. We assessed statin adherence in Swedish primary...

Temporal deep learning for 28-day mortality prediction in critically ill ovarian cancer patients: a multicenter development and validation study using hourly vital signs.

BACKGROUND: Ovarian cancer patients requiring intensive care unit (ICU) admission face particularly grave prognosis, yet current prognostic models rel...

Echocardiographic assessment in arterial hypertension: a contemporary narrative review.

Arterial hypertension remains the leading modifiable cause of cardiovascular morbidity and mortality resulting in characteristic changes in cardiac st...

Predictive Model for Thrombosis in Mature Autologous Arteriovenous Fistula.

OBJECTIVE: Based on the "high-risk stenosis-thrombosis" theory, this study developed a risk prediction model for thrombosis in mature autogenous arter...

Decoding the Complexity of Tricuspid Regurgitation Prognostic Drivers Using Explainable Artificial Intelligence.

AIMS: Prognosis in tricuspid regurgitation (TR) is shaped by complex clinical and hemodynamic interactions, complicating risk stratification. We aimed...

Retinal vascular parameters as AI-driven biomarkers for pulse wave velocity assessment: a telemedicine strategy for cardiovascular risk assessment.

BACKGROUND: Pulse wave velocity (PWV), known as the gold standard for evaluating arterial stiffness, is limited by device dependence. This study aims ...

Prediction of hypertension and restenosis under guideline-directed management in aortic coarctation: development and validation of machine-learning models.

BACKGROUND: Aortic coarctation (CoA) is a major cause of arterial hypertension in young individuals, with recurrence occurring in up to one-third of p...

Machine learning-based early kidney stone risk classification in diabetic-hypertensive patients using obesity composite indices.

Kidney stone disease (KSD) is increasingly prevalent among patients with diabetes mellitus and hypertension. Obesity-related metabolic abnormalities m...

The diagnostic and predictive value of AI-combined multilayer spiral CT for MACE after emergency PCI in STEMI patients: A prospective cohort study.

ST-segment elevation myocardial infarction (STEMI) patients remain at substantial risk for major adverse cardiovascular events (MACE) following emerge...

A Machine-Learning Model for Accurate Diuresis Prediction in Acute Heart Failure (DRC-AHF).

AIMS: We aimed to develop a machine learning-based tool for accurate quantitative prediction of diuretic response in acute heart failure (AHF). METHOD...