Congestive Heart Failure

Diabetic retinopathy-recommendations for screening and treatment.

Diabetic retinopathy (DR), the prevalence of which continues to rise, is one of the most common causes of vision loss worldwide. Experimental and clinical research in recent years has contributed to a better understanding of the pathogenesis of DR, which is complex and results from many interrelated processes leading to abnormal permeability and occlusion of the retinal vasculature, with ischemia ...

Mitral annular plane systolic excursion to left atrial volume ratio - a strainless relation with left ventricular filling pressures.

Left atrial reservoir strain (LASr) offers diagnostic and prognostic value in patients with heart failure. However, LASr may be technically challenging and is not available to all clinical echocardiographers. Since LASr is a consequence of left atrial (LA) stretch during apical descent of the mitral annulus, we hypothesized that a ratio between mitral annular plane systolic excursion (MAPSE) and L...

Deep learning-based evaluation of the severity of mitral regurgitation in canine myxomatous mitral valve disease patients using digital stethoscope recordings.

BACKGROUND: Myxomatous mitral valve disease (MMVD) represents the most prevalent cardiac disorder in dogs, frequently resulting in mitral regurgitatio...

Artificial Intelligence in the Management of Heart Failure.

Artificial intelligence (AI) has the potential to revolutionize the management of heart failure. AI-based tools can guide the diagnosis and treatment ...

Hydroalcoholic extract from Syagrus cocoides Martius almonds (ariri coconut) induces diuretic effects in rodents.

BACKGROUND: Recent studies point to the great biotechnological potential of Syagrus cocoides as a tool for treating cardiovascular disorders.

Machine learning models for acute kidney injury prediction and management: a scoping review of externally validated studies.

Despite advancements in medical care, acute kidney injury (AKI) remains a major contributor to adverse patient outcomes and presents a significant cha...

Predicting Visual Acuity after Retinal Vein Occlusion Anti-VEGF Treatment: Development and Validation of an Interpretable Machine Learning Model.

Accurate prediction of post-treatment visual acuity in macular edema secondary to retinal vein occlusion (RVO-ME) is critical for optimizing anti-VEGF...

Protein interactions, network pharmacology, and machine learning work together to predict genes linked to mitochondrial dysfunction in hypertrophic cardiomyopathy.

This study looked at possible targets for hypertrophic cardiomyopathy (HCM), a condition marked by thickening of the ventricular wall, primarily in th...

Deep learning for automated segmentation of brain edema in meningioma after radiosurgery.

BACKGROUND: Although gamma Knife radiosurgery (GKRS) is commonly used to treat benign brain tumors, such as meningioma, irradiating the surrounding br...

Predicting mortality and risk factors of sepsis related ARDS using machine learning models.

Sepsis related acute respiratory distress syndrome (ARDS) is a common and serious disease in clinic. Accurate prediction of in-hospital mortality of p...

Unsupervised machine learning analysis of optical coherence tomography radiomics features for predicting treatment outcomes in diabetic macular edema.

This study aimed to identify distinct clusters of diabetic macular edema (DME) patients with differential anti-vascular endothelial growth factor (VEG...

A machine learning-based severity stratification tool for high altitude pulmonary edema.

This study aimed to identify key predictors for the severity of High Altitude Pulmonary Edema (HAPE) to assist clinicians in promptly recognizing seve...

U-shaped network combining dual-stream fusion mamba and redesigned multilayer perceptron for myocardial pathology segmentation.

BACKGROUND: Cardiac magnetic resonance imaging (CMR) provides critical pathological information, such as scars and edema, which are vital for diagnosi...

EFCNet enhances the efficiency of segmenting clinically significant small medical objects.

Efficient segmentation of small hyperreflective dots, key biomarkers for diseases like macular edema, is critical for diagnosis and treatment monitori...

Deep learning models for segmenting phonocardiogram signals: a comparative study.

Cardiac auscultation requires the mechanical vibrations occurring on the body's surface, which carries a range of sound frequencies. These sounds are ...

Diagnostic and prognostic significance of miRNA-15a-5p, 16-5p, and 92a-3p in arrhythmogenic right ventricular cardiomyopathy.

BACKGROUND: Arrhythmogenic right ventricular cardiomyopathy (ARVC) presents diagnostic challenges and significant clinical burden because of life-thre...

Deep learning-based classification of lymphedema and other lower limb edema diseases using clinical images.

Lymphedema is a chronic condition characterized by lymphatic fluid accumulation, primarily affecting the limbs. Its diagnosis is challenging due to sy...

AI-based measurement of cardiothoracic ratio in chest X-rays and prediction of echocardiographic congestive heart failure.

BACKGROUND: This study presents an artificial intelligence (AI) model for automated cardiothoracic ratio (CTR) measurement from chest X-rays (CXRs) an...

Identification of Patients With Congestive Heart Failure From the Electronic Health Records of Two Hospitals: Retrospective Study.

BACKGROUND: Congestive heart failure (CHF) is a common cause of hospital admissions. Medical records contain valuable information about CHF, but manua...

Semantic-consistent diffusion model for unsupervised traumatic brain injury detection and segmentation from computed tomography images.

BACKGROUND: Unsupervised traumatic brain injury (TBI) lesion detection aims to identify and segment abnormal regions, such as cerebral edema and hemor...