Congestive Heart Failure

Non-pharmacological treatment of AECOPD.

Acute exacerbations of chronic obstructive pulmonary disease (AECOPDs) are acute events characterized by rapid worsening of dyspnea, cough, and sputum production, often leading to gas exchange impairment, ventilatory failure, and hospitalization. While pharmacological therapy remains central for managing the acute phase, non-pharmacological interventions play a crucial role in stabilizing patients...

Contextual anatomy-guided deep learning for accurate fovea segmentation in diabetic retinopathy fundus images.

Accurate fovea segmentation in fundus images is a critical step in diabetic retinopathy screening; however, it remains a challenging task due to the indistinct boundaries of the fovea. Beyond simple localization, precise segmentation offers essential clinical value for Diabetic Macular Edema (DME) management, as treatment decisions-specifically the choice between intravitreal anti-VEGF injection f...

Natriuretic peptides and cardiac troponins in hypertension and heart failure: A contemporary review of the evidence.

Hypertension is the second leading cause of heart failure (HF), yet strategies for identifying hypertensive individuals at increased HF risk remain li...

Machine learning models enhance detection of arrhythmogenic right ventricular cardiomyopathy.

Arrhythmogenic right ventricular cardiomyopathy (ARVC) is a heritable cardiac disorder associated with sudden cardiac death, yet its diagnosis remains...

Feasibility of two-dimensional multi-segmented late gadolinium enhancement combined with artificial intelligence reconstruction deep-learning noise reduction in patients with non-ischemic cardiomyopathy.

OBJECTIVE: Although two-dimensional (2D) single-segmented late gadolinium enhancement (2D-SSLGE) sequences are the gold standard for LGE acquisition, ...

A machine learning model based on routine blood-derived indices for early arterial stiffness prediction in the community.

BACKGROUND: Increased arterial stiffness is a high-risk factor for cardiovascular diseases, making its early identification crucial. Based on data fro...

Acute deep neck infection MRI: deep learning segmentation and clinical relevance of retropharyngeal edema volume.

OBJECTIVE: Retropharyngeal edema (RPE) on MRI in patients with acute neck infection is associated with disease severity. We explored the potential rol...

Association of allostatic load index with cognitive impairment in high-risk stroke populations.

BACKGROUND: Chronic psychosocial stress induces cumulative physiological dysregulation that accelerates biological aging and contributes to the develo...

Predicting adverse outcomes in dilated cardiomyopathy using 3D echocardiography: penalised Cox regression versus machine learning.

BACKGROUND: Risk prediction in dilated cardiomyopathy (DCM) remains suboptimal, and there is uncertainty about how newer machine-learning (ML) methods...

Machine learning-based prediction of persistent diabetic macular edema using OCT-derived structural biomarkers and clinical features.

BACKGROUND: Persistent diabetic macular edema (DME) remains a leading cause of vision loss in diabetic retinopathy, even with anti-VEGF therapy. About...

Cardiac Magnetic Resonance Scan Efficiency.

Accelerated 30 min cardiac magnetic resonance (CMR) examinations combining cutting-edge acquisition, reconstruction, and workflow methods have the pot...

Strain Imaging in Heart Failure.

Strain imaging, performed with echocardiography and cardiovascular magnetic resonance (CMR), is a noninvasive technique for detecting subclinical myoc...

Blood pressure estimation using single photoplethysmography signal based on ResNet-BiGRU.

OBJECTIVES: Conventional cuff-based blood pressure (BP) measurement provides only intermittent readings, whereas photoplethysmography (PPG)-based meth...

Enhanced cardiovascular disease risk prediction using integrated machine learning models: a study from the UK Biobank cohort.

OBJECTIVE: Cardiovascular diseases (CVD) remain the leading cause of mortality globally, necessitating early risk identification to improve prevention...

Clinical History, Spirometry, and CT Features Can Predict Dyspnea in Smokers with and without Spirometry-Defined COPD.

Building an Extracorporeal Membrane Oxygenation Digital Twin Using High-Resolution Patient Data: An artificial intelligence model for virtual reality simulation.

OBJECTIVES: Extracorporeal membrane oxygenation (ECMO) is a life-saving therapy for severe cardiopulmonary failure, but structured training remains co...

Predicting cardiopulmonary exercise testing outcomes in congenital heart disease through multimodal data integration and geometric learning.

Cardiopulmonary exercise testing (CPET) provides a comprehensive assessment of functional capacity by measuring key physiological variables including ...

Integrating machine learning and SHAP analysis to identify the risk of cognitive frailty in older adults with chronic heart failure.

BACKGROUND: Accurately identifying older adults with chronic heart failure (CHF) at high risk of cognitive frailty is crucial for timely preventive in...

Prediction of hemorrhagic transformation after intravenous thrombolysis in acute anterior circulation ischemic stroke through radiomics-based machine learning models.

BACKGROUND: Hemorrhagic transformation (HT) and its associated neurological deterioration remain major concerns that limit decision-making in thrombol...

Optimization and External Validation of a Deep Learning Model for Segmentation and Quantification of Traumatic Brain Injury Lesions on Head Computed Tomography.

Current methods for radiological classification in traumatic brain injury (TBI), such as the Marshall and Rotterdam score, provide an incomplete descr...