Congestive Heart Failure

Multimodality Risk Assessment of Patients with Ischemic Heart Disease Using Deep Learning Models Applied to Electrocardiograms and Chest X-rays.

Comprehensive management approaches for patients with ischemic heart disease (IHD) are important aids for prognostication and treatment planning. While single-modality deep neural networks (DNNs) have shown promising performance for detecting cardiac abnormalities, the potential benefits of using DNNs for multimodality risk assessment in patients with IHD have not been reported. The purpose of thi...

Fine grained automatic left ventricle segmentation via ROI based Tri-Convolutional neural networks.

BACKGROUND: The left ventricle segmentation (LVS) is crucial to the assessment of cardiac function. Globally, cardiovascular disease accounts for the majority of deaths, posing a significant health threat. In recent years, LVS has gained important attention due to its ability to measure vital parameters such as myocardial mass, end-diastolic volume, and ejection fraction. Medical professionals rea...

Advanced Machine Learning Models for Predicting Post-Thrombolysis Hemorrhagic Transformation in Acute Ischemic Stroke Patients: A Systematic Review and Meta-Analysis.

Thrombolytic therapy is essential for acute ischemic stroke (AIS) management but poses a risk of hemorrhagic transformation (HT), necessitating accur...

Assessment of Osteoprotegerin and Receptor Activator of Nf-Κb Ligand in Malaysian Male Patients with Chronic Obstructive Pulmonary Disease: A Cross-Sectional Study.

Background: Limited information exists regarding the pathophysiological interactions between osteoporosis and chronic obstructive pulmonary disease (C...

[Advances in chest imaging in early chronic obstructive pulmonary disease].

Chronic obstructive pulmonary disease(COPD)is a heterogeneous and complex disease, and is characterized by exertional dyspnea and chronic cough. For m...

Deep learning-derived 12-lead electrocardiogram-based genotype prediction for hypertrophic cardiomyopathy: a pilot study.

Given the psychosocial and ethical burden, patients with hypertrophic cardiomyopathy (HCMs) could benefit from the establishment of genetic probabili...

Skin Changes in Suspected Lyme Disease.

Dear Editor, Ticks carry many diseases, bacteria, and viruses and represent a very important healthcare issue both in Croatia and globally. Although m...

Successful robot-assisted laparoscopic resection of pheochromocytoma in a patient with dilated cardiomyopathy: A case report on extremely high-risk anesthesia management.

RATIONALE: Anesthetic management during resection of pheochromocytoma is a huge challenge, especially when accompanied by dilated cardiomyopathy (DCM)...

Musculoskeletal CT Imaging: State-of-the-Art Advancements and Future Directions.

CT is one of the most widely used modalities for musculoskeletal imaging. Recent advancements in the field include the introduction of four-dimensiona...

Deep Learning for Premature Ventricular Contraction-Cardiomyopathy: Are We Digging Deep Enough?

Demographic Information Fusion Using Attentive Pooling In CNN-GRU Model For Systolic Blood Pressure Estimation.

Fusing demographic information into deep learning models has become of interest in recent end-to-end cuff-less blood pressure (BP) estimation studies ...

Deep learning-based detection of diabetic macular edema using optical coherence tomography and fundus images: A meta-analysis.

Diabetic macular edema (DME) is an important cause of visual impairment in the working-age group. Deep learning methods have been developed to detect ...

Hepatic Hydrothorax Without Ascites: A Diagnostic And Management Challenge.

Hepatic hydrothorax refers to the presence of a pleural effusion (usually >500 mL) in a patient with cirrhosis who does not have other reasons to have...

[A deep-learning model for the assessment of coronary heart disease and related risk factors via the evaluation of retinal fundus photographs].

To develop and validate a deep learning model based on fundus photos for the identification of coronary heart disease (CHD) and associated risk facto...

Automated Measurement of Native T1 and Extracellular Volume Fraction in Cardiac Magnetic Resonance Imaging Using a Commercially Available Deep Learning Algorithm.

OBJECTIVE: T1 mapping provides valuable information regarding cardiomyopathies. Manual drawing is time consuming and prone to subjective errors. There...

Deep Learning Model for the Detection of Corneal Edema Before Descemet Membrane Endothelial Keratoplasty on Optical Coherence Tomography Images.

PURPOSE: Descemet membrane endothelial keratoplasty (DMEK) is the preferred method for treating corneal endothelial dysfunction, such as Fuchs endothe...

Clinician Adoption of an Artificial Intelligence Algorithm to Detect Left Ventricular Systolic Dysfunction in Primary Care.

OBJECTIVE: To compare the clinicians' characteristics of "high adopters" and "low adopters" of an artificial intelligence (AI)-enabled electrocardiogr...

DeepHisCoM: deep learning pathway analysis using hierarchical structural component models.

Many statistical methods for pathway analysis have been used to identify pathways associated with the disease along with biological factors such as ge...

Etiology of Macular Edema Defined by Deep Learning in Optical Coherence Tomography Scans.

PURPOSE: To develop an automated method based on deep learning (DL) to classify macular edema (ME) from the evaluation of optical coherence tomography...

[Transoral robotic surgery for treatment of lingual thyroglossal duct cyst].

To investigate the feasibility, safety and efficacy of transoral robotic surgery (TORS) in the treatment of lingual thyroglossal duct cyst (LTGDC). ...