Congestive Heart Failure

Automated Echocardiographic Quantification of Left Ventricular Ejection Fraction Without Volume Measurements Using a Machine Learning Algorithm Mimicking a Human Expert.

BACKGROUND: Echocardiographic quantification of left ventricular (LV) ejection fraction (EF) relies on either manual or automated identification of endocardial boundaries followed by model-based calculation of end-systolic and end-diastolic LV volumes. Recent developments in artificial intelligence resulted in computer algorithms that allow near automated detection of endocardial boundaries and me...

NOVEL MUTATIONS IN AN INFANT WITH MICROCEPHALIC PRIMORDIAL DWARFISM, DILATED CARDIOMYOPATHY, SUBCLINICAL HYPOTHYROIDISM, AND EARLY DEATH: EXPANDING THE PHENOTYPE OF MUTATIONS.

OBJECTIVE: Microcephalic primordial dwarfism (MPD) is a group of clinically and genetically heterogeneous disorders which result in severe prenatal and postnatal growth failure. X-ray repair cross-complementing protein 4 () is a causative gene for an autosomal recessive form of MPD. The objective of this report is to describe novel mutations in a female infant with MPD, dilated cardiomyopathy, an...

Prediction model development of late-onset preeclampsia using machine learning-based methods.

Preeclampsia is one of the leading causes of maternal and fetal morbidity and mortality. Due to the lack of effective preventive measures, its predict...

Localizing B-Lines in Lung Ultrasonography by Weakly Supervised Deep Learning, In-Vivo Results.

Lung ultrasound (LUS) is nowadays gaining growing attention from both the clinical and technical world. Of particular interest are several imaging-art...

SVR ensemble-based continuous blood pressure prediction using multi-channel photoplethysmogram.

In this paper, a continuous non-occluding blood pressure (BP) prediction method is proposed using multiple photoplethysmogram (PPG) signals. In the ne...

Readmission prediction using deep learning on electronic health records.

Unscheduled 30-day readmissions are a hallmark of Congestive Heart Failure (CHF) patients that pose significant health risks and escalate care cost. I...

Deep Learning Fundus Image Analysis for Diabetic Retinopathy and Macular Edema Grading.

Diabetes is a globally prevalent disease that can cause visible microvascular complications such as diabetic retinopathy and macular edema in the huma...

An Automatic Approach Using ELM Classifier for HFpEF Identification Based on Heart Sound Characteristics.

Heart failure with preserved ejection fraction (HFpEF) is a complex and heterogeneous clinical syndrome. For the purpose of assisting HFpEF diagnosis,...

Development of "Predict ME," an online classifier to aid in differentiating diabetic macular edema from pseudophakic macular edema.

PURPOSE: Differentiating the underlying pathology of macular edema in patients with diabetic retinopathy following cataract surgery can be challenging...

Is Cardiac Troponin I Valuable to Detect Low-Level Myocardial Damage in Congestive Heart Failure?

OBJECTIVES: Congestive heart failure (CHF) is a heart disease with a growing incidence and prevalence. Creatine kinase-myocardial base (CK-MB) is gene...

Brain Tumour Segmentation Using Convolutional Neural Network with Tensor Flow.

Introduction: The determination of tumour extent is a major challenging task in brain tumour planning and quantitative evaluation. Magnetic Resonance ...

Identification of a Multiplex Biomarker Panel for Hypertrophic Cardiomyopathy Using Quantitative Proteomics and Machine Learning.

Hypertrophic cardiomyopathy (HCM) is defined by pathological left ventricular hypertrophy (LVH). It is the commonest inherited cardiac condition and a...

Leveraging Machine Learning Techniques to Forecast Patient Prognosis After Percutaneous Coronary Intervention.

OBJECTIVES: This study sought to determine whether machine learning can be used to better identify patients at risk for death or congestive heart fail...

Using machine learning to predict one-year cardiovascular events in patients with severe dilated cardiomyopathy.

PURPOSE: Dilated cardiomyopathy (DCM) is a common form of cardiomyopathy and it is associated with poor outcomes. A poor prognosis of DCM patients wit...

A Non-Invasive Continuous Blood Pressure Estimation Approach Based on Machine Learning.

Considering the existing issues of traditional blood pressure (BP) measurement methods and non-invasive continuous BP measurement techniques, this stu...

Reproduction study using public data of: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs.

We have attempted to reproduce the results in Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal...

Exploiting Epistemic Uncertainty of Anatomy Segmentation for Anomaly Detection in Retinal OCT.

Diagnosis and treatment guidance are aided by detecting relevant biomarkers in medical images. Although supervised deep learning can perform accurate ...

Prediction of Nephropathy in Type 2 Diabetes: An Analysis of the ACCORD Trial Applying Machine Learning Techniques.

Applying data mining and machine learning (ML) techniques to clinical data might identify predictive biomarkers for diabetic nephropathy (DN), a commo...

A Precision Environment-Wide Association Study of Hypertension via Supervised Cadre Models.

We consider the problem in precision health of grouping people into subpopulations based on their degree of vulnerability to a risk factor. These subp...

Ventricular geometry-regularized QRSd predicts cardiac resynchronization therapy response: machine learning from crosstalk between electrocardiography and echocardiography.

Up to one-third of patients selected by current guidelines do not respond to cardiac resynchronization therapy (CRT), the aim of this study was to fin...