Congestive Heart Failure

Uncertainty-Aware Deep Learning With Cross-Task Supervision for PHE Segmentation on CT Images.

Perihematomal edema (PHE) volume, surrounding spontaneous intracerebral hemorrhage (SICH), is an imp...

Integrated Learning Model-Based Assessment of Enteral Nutrition Support in Neurosurgical Intensive Care Patients.

To observe the clinical efficacy of early enteral nutrition application in critically ill neurosurgi...

Follow-Up of Robotic Mitral Valve Repair: A Single Tertiary Institution Experience in China.

Surgical treatment of mitral valve diseases has become minimally invasive. This study analyzed the f...

Evaluating machine learning classifiers for glaucoma referral decision support in primary care settings.

Several artificial intelligence algorithms have been proposed to help diagnose glaucoma by analyzing...

Prediction of serious outcomes based on continuous vital sign monitoring of high-risk patients.

Continuous monitoring of high-risk patients and early prediction of severe outcomes is crucial to pr...

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 i...

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 musculoske...

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...

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 help...

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...

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 ...

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) ...

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-L...

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...

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 algor...

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

Spontaneous intracerebral hemorrhage (ICH) has high morbidity and mortality. Computed tomography (CT...

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 ef...

Application of Deep Learning Technology in Glioma.

A common and most basic brain tumor is glioma that is exceptionally dangerous to health of various p...

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...