Congestive Heart Failure

Automated extraction of sudden cardiac death risk factors in hypertrophic cardiomyopathy patients by natural language processing.

BACKGROUND: The management of hypertrophic cardiomyopathy (HCM) patients requires the knowledge of risk factors associated with sudden cardiac death (SCD). SCD risk factors such as syncope and family history of SCD (FH-SCD) as well as family history of HCM (FH-HCM) are documented in electronic health records (EHRs) as clinical narratives. Automated extraction of risk factors from clinical narrativ...

Automated segmentation of macular edema in OCT using deep neural networks.

Macular edema is an eye disease that can affect visual acuity. Typical disease symptoms include subretinal fluid (SRF) and pigment epithelium detachment (PED). Optical coherence tomography (OCT) has been widely used for diagnosing macular edema because of its non-invasive and high resolution properties. Segmentation for macular edema lesions from OCT images plays an important role in clinical diag...

Statistical Approaches Based on Deep Learning Regression for Verification of Normality of Blood Pressure Estimates.

Oscillometric blood pressure (BP) monitors currently estimate a single point but do not identify variations in response to physiological characteristi...

An Efficient Cardiac Arrhythmia Onset Detection Technique Using a Novel Feature Rank Score Algorithm.

The interpretation of various cardiovascular blood flow abnormalities can be identified using Electrocardiogram (ECG). The predominant anomaly due to ...

Deep learning in ophthalmology: The technical and clinical considerations.

The advent of computer graphic processing units, improvement in mathematical models and availability of big data has allowed artificial intelligence (...

Directional Relationship Between Vitamin D Status and Prediabetes: A New Approach from Artificial Neural Network in a Cohort of Workers with Overweight-Obesity.

Despite the increasing literature on the association of diabetes with inflammation, cardiovascular risk, and vitamin D (25(OH)D) concentrations, stro...

A Novel Hardware Systolic Architecture of a Self-Organizing Map Neural Network.

In this article, we propose to design a new modular architecture for a self-organizing map (SOM) neural network. The proposed approach, called systoli...

Prospective validation of a deep learning electrocardiogram algorithm for the detection of left ventricular systolic dysfunction.

OBJECTIVES: We sought to validate a deep learning algorithm designed to predict an ejection fraction (EF) less than or equal to 35% based on the 12-le...

Estimation of echocardiogram parameters with the aid of impedance cardiography and artificial neural networks.

The advent of cardiovascular diseases as a disease of mass catastrophy, in recent years is alarming. It is expected to spread as an epidemic by 2030. ...

Identifying Ventricular Arrhythmias and Their Predictors by Applying Machine Learning Methods to Electronic Health Records in Patients With Hypertrophic Cardiomyopathy (HCM-VAr-Risk Model).

Clinical risk stratification for sudden cardiac death (SCD) in hypertrophic cardiomyopathy (HC) employs rules derived from American College of Cardiol...

Machine Learning Accurately Predicts Short-Term Outcomes Following Open Reduction and Internal Fixation of Ankle Fractures.

Ankle fractures are common orthopedic injuries with favorable outcomes when managed with open reduction and internal fixation (ORIF). Several patient-...

Deep Learning for Segmentation Using an Open Large-Scale Dataset in 2D Echocardiography.

Delineation of the cardiac structures from 2D echocardiographic images is a common clinical task to establish a diagnosis. Over the past decades, the ...

Deep-learning based multiclass retinal fluid segmentation and detection in optical coherence tomography images using a fully convolutional neural network.

As a non-invasive imaging modality, optical coherence tomography (OCT) can provide micrometer-resolution 3D images of retinal structures. These images...

A Highly Sensitive Pressure-Sensing Array for Blood Pressure Estimation Assisted by Machine-Learning Techniques.

This work describes the development of a pressure-sensing array for noninvasive continuous blood pulse-wave monitoring. The sensing elements comprise ...

Quantifying lung ultrasound comets with a convolutional neural network: Initial clinical results.

Lung ultrasound comets are "comet-tail" artifacts appearing in lung ultrasound images. They are particularly useful in detecting several lung patholog...

A Large Animal Model of Right Ventricular Failure due to Chronic Thromboembolic Pulmonary Hypertension: A Focus on Function.

Chronic thromboembolic pulmonary hypertension (CTEPH) is a debilitating disease that progresses to right ventricular (RV) failure and death if left un...

Fully automated detection of retinal disorders by image-based deep learning.

PURPOSE: With the aging population and the global diabetes epidemic, the prevalence of age-related macular degeneration (AMD) and diabetic macular ede...

Estimation of constitutive parameters of the aortic wall using a machine learning approach.

The patient-specific biomechanical analysis of the aorta requires the quantification of the mechanical properties of individual patients. Current inv...

Cardiac Phase Detection in Echocardiograms With Densely Gated Recurrent Neural Networks and Global Extrema Loss.

Accurate detection of end-systolic (ES) and end-diastolic (ED) frames in an echocardiographic cine series can be difficult but necessary pre-processin...

Accuracy of B-natriuretic peptide for the diagnosis of decompensated heart failure in muscular dystrophies patients with chronic respiratory failure.

Heart failure and restrictive respiratory insufficiency are complications in muscular dystrophies. We aimed to assess the accuracy of the B-natriureti...