Congestive Heart Failure

Exploiting exercise electrocardiography to improve early diagnosis of atrial fibrillation with deep learning neural networks.

Atrial fibrillation (AF) is the most common type of sustained arrhythmia. It results from abnormal irregularities in the electrical performance of the atria, and may cause heart thrombosis, stroke, arterial disease, thromboembolism, and heart failure. Prior to the onset of atrial fibrillation, most people experience atrial cardiomyopathy which, if effectively managed, can be prevented from progres...

Diagnostic performance for detecting bone marrow edema of the hip on dual-energy CT: Deep learning model vs. musculoskeletal physicians and radiologists.

PURPOSE: To compare the diagnostic performance of a deep learning (DL) model with that of musculoskeletal physicians and radiologists for detecting bone marrow edema on dual-energy CT (DECT).

Deep learning algorithms for detection of diabetic macular edema in OCT images: A systematic review and meta-analysis.

PURPOSE: Artificial intelligence (AI) can detect diabetic macular edema (DME) from optical coherence tomography (OCT) images. We aimed to evaluate the...

A new deep learning framework based on blood pressure range constraint for continuous cuffless BP estimation.

Blood pressure (BP) is known as an indicator of human health status, and regular measurement is helpful for early detection of cardiovascular diseases...

Recent Advanced Deep Learning Architectures for Retinal Fluid Segmentation on Optical Coherence Tomography Images.

With non-invasive and high-resolution properties, optical coherence tomography (OCT) has been widely used as a retinal imaging modality for the effect...

Identification of Characteristic Points in Multivariate Physiological Signals by Sensor Fusion and Multi-Task Deep Networks.

Identification of characteristic points in physiological signals, such as the peak of the R wave in the electrocardiogram and the peak of the systolic...

Deep Learning-Based Electrocardiograph in Evaluating Radiofrequency Ablation for Rapid Arrhythmia.

This study is aimed at analyzing the important role of deep learning-based electrocardiograph (ECG) in the efficacy evaluation of radiofrequency ablat...

Fuzzy C-Means Algorithm-Based ARM-Linux-Embedded System Combined with Magnetic Resonance Imaging for Progression Prediction of Brain Tumors.

The aim of this research was to analyze the application of fuzzy C-means (FCM) algorithm-based ARM-Linux-embedded system in magnetic resonance imaging...

Update on risk factors and biomarkers of sudden unexplained cardiac death.

Sudden cardiac death (SCD) accounts for approximately 15%-20% of all deaths worldwide, the causes of which are mainly structural heart diseases. Howev...

Deep Learning Algorithm-Based MRI Image in the Diagnosis of Diabetic Macular Edema.

This study investigates the value of magnetic resonance imaging (MRI) based on a deep learning algorithm in the diagnosis of diabetic macular edema (D...

Advances in computed tomography-based prognostic methods for intracerebral hemorrhage.

Spontaneous intracerebral hemorrhage (ICH) has high morbidity and mortality. Computed tomography (CT) plays an important role in the diagnosis, treatm...

Predicting adverse cardiac events in sarcoidosis: deep learning from automated characterization of regional myocardial remodeling.

Recognizing early cardiac sarcoidosis (CS) imaging phenotypes can help identify opportunities for effective treatment before irreversible myocardial p...

Application of Deep Learning Technology in Glioma.

A common and most basic brain tumor is glioma that is exceptionally dangerous to health of various patients. A glioma segmentation, which is primarily...

Subcutaneous emphysema and negative-pressure pulmonary edema after robot-assisted bladder enlargement: A case report.

Artificial Intelligence Detection of Left Ventricular Systolic Dysfunction Using Chest X-Rays: Prospective Validation, Please.

Improving robustness of automatic cardiac function quantification from cine magnetic resonance imaging using synthetic image data.

Although having been the subject of intense research over the years, cardiac function quantification from MRI is still not a fully automatic process i...

Iron Deficiency Impacts Diastolic Function, Aerobic Exercise Capacity, and Patient Phenotyping in Heart Failure With Preserved Ejection Fraction: A Subanalysis of the OptimEx-Clin Study.

AIMS: Iron deficiency (ID) is linked to reduced aerobic exercise capacity and poor prognosis in patients with heart failure (HF) with reduced ejection...

A bibliometric review of peripartum cardiomyopathy compared to other cardiomyopathies using artificial intelligence and machine learning.

As developments in artificial intelligence and machine learning become more widespread in healthcare, their potential to transform clinical outcomes a...

Automated In-Line Artificial Intelligence Measured Global Longitudinal Shortening and Mitral Annular Plane Systolic Excursion: Reproducibility and Prognostic Significance.

Background Global longitudinal shortening (GL-Shortening) and the mitral annular plane systolic excursion (MAPSE) are known markers in heart failure p...

Effects of age, gender, and hemisphere on cerebrovascular hemodynamics in children and young adults: Developmental scores and machine learning classifiers.

A constant blood supply to the brain is required for mental function. Research with Doppler ultrasonography has important clinical value and burgeonin...