Congestive Heart Failure

A Novel Automated Blood Pressure Estimation Algorithm Using Sequences of Korotkoff Sounds.

The use of automated non-invasive blood pressure (NIBP) measurement devices is growing, as they can ...

Machine Learning-Based Multiparametric Magnetic Resonance Imaging Radiomics for Prediction of H3K27M Mutation in Midline Gliomas.

OBJECTIVE: H3K27M mutation in gliomas has prognostic implications. Previous magnetic resonance imagi...

Subfoveal choroidal thickness changes after intravitreal ranibizumab injections in different patterns of diabetic macular edema using a deep learning-based auto-segmentation.

PURPOSE: To evaluate the effect of intravitreal injection of ranibizumab (IVR) on subfoveal choroida...

Blood Pressure Morphology Assessment from Photoplethysmogram and Demographic Information Using Deep Learning with Attention Mechanism.

Arterial blood pressure (ABP) is an important vital sign from which it can be extracted valuable inf...

Artificial Intelligence and Left Ventricular Diastolic Function Assessment: A New Tool for Improved Practice?

A deep learning methodology for the automated detection of end-diastolic frames in intravascular ultrasound images.

Coronary luminal dimensions change during the cardiac cycle. However, contemporary volumetric intrav...

A deep-learning semantic segmentation approach to fully automated MRI-based left-ventricular deformation analysis in cardiotoxicity.

Left-ventricular (LV) strain measurements with the Displacement Encoding with Stimulated Echoes (DEN...

Predicting COVID-19 disease progression and patient outcomes based on temporal deep learning.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has caused health concerns worldwide si...

Artificial intelligence-enhanced electrocardiography in cardiovascular disease management.

The application of artificial intelligence (AI) to the electrocardiogram (ECG), a ubiquitous and sta...

Physical Features and Vital Signs Predict Serum Albumin and Globulin Concentrations Using Machine Learning.

OBJECTIVE: Serum protein concentrations are diagnostically and prognostically valuable in cancer and...

Extracting Angina Symptoms from Clinical Notes Using Pre-Trained Transformer Architectures.

Anginal symptoms can connote increased cardiac risk and a need for change in cardiovascular manageme...

Artificial intelligence matches subjective severity assessment of pneumonia for prediction of patient outcome and need for mechanical ventilation: a cohort study.

To compare the performance of artificial intelligence (AI) and Radiographic Assessment of Lung Edema...

Foveal avascular zone segmentation in optical coherence tomography angiography images using a deep learning approach.

The purpose of this study was to introduce a new deep learning (DL) model for segmentation of the fo...

Explanatory Analysis of a Machine Learning Model to Identify Hypertrophic Cardiomyopathy Patients from EHR Using Diagnostic Codes.

Hypertrophic cardiomyopathy (HCM) is a genetic heart disease that is the leading cause of sudden car...

Deep learning shows good reliability for automatic segmentation and volume measurement of brain hemorrhage, intraventricular extension, and peripheral edema.

OBJECTIVES: To evaluate for the first time the performance of a deep learning method based on no-new...

Open-Source Automatic Segmentation of Ocular Structures and Biomarkers of Microbial Keratitis on Slit-Lamp Photography Images Using Deep Learning.

We propose a fully-automatic deep learning-based algorithm for segmentation of ocular structures and...

Retraining Convolutional Neural Networks for Specialized Cardiovascular Imaging Tasks: Lessons from Tetralogy of Fallot.

Ventricular contouring of cardiac magnetic resonance imaging is the gold standard for volumetric ana...

External validation of a deep learning electrocardiogram algorithm to detect ventricular dysfunction.

OBJECTIVE: To validate a novel artificial-intelligence electrocardiogram algorithm (AI-ECG) to detec...

Quantitative analysis of blood cells from microscopic images using convolutional neural network.

Blood cell count provides relevant clinical information about different kinds of disorders. Any devi...

Learning fine-grained estimation of physiological states from coarse-grained labels by distribution restoration.

Due to its importance in clinical science, the estimation of physiological states (e.g., the severit...

Automatic recognition of murmurs of ventricular septal defect using convolutional recurrent neural networks with temporal attentive pooling.

Recognizing specific heart sound patterns is important for the diagnosis of structural heart disease...