Hematology

Deep learning for prediction of cardiac indices from photoplethysmographic waveform: A virtual database approach.

Deep learning methods combined with large datasets have recently shown significant progress in solving several medical tasks. However, collecting and annotating large datasets can be a very cumbersome and expensive task. We tackle these problems with a virtual database approach where training data is generated using computer simulations of related phenomena. Specifically, we concentrate on the fol...

Prediction of stenosis behaviour in artery by neural network and multiple linear regressions.

Blood flow analysis in the artery is a paramount study in the field of arterial stenosis evaluation. Studies conducted so far have reported the analysis of blood flow parameters using different techniques, but the regression analysis is not adequately used. Artificial neural network is a nonlinear and nonparametric approach. It uses back-propagation algorithm for regression analysis, which is effe...

Decreased Hemoglobin Concentration and Iron Metabolism Disorder in Periodontitis: Systematic Review and Meta-Analysis.

Periodontitis is a chronic inflammatory disease with a possible infectious component. Anemia of inflammation (AI) occurring in various chronic diseas...

Regular cold water swimming during winter time affects resting hematological parameters and serum erythropoietin.

Recreational winter swimming in cold sea water evokes body responses to regularly repeated cold water immersion. However, the understanding of adaptiv...

New Insights and Methods in the Approach to Thalassemia Major: The Lesson From the Case of Adrenal Insufficiency.

Thalassemia Major (TM) is a complex pathology that needs a highly skilled approach. Endocrine comorbidities are nowadays the most important complicat...

Classification of stomach infections: A paradigm of convolutional neural network along with classical features fusion and selection.

Automated detection and classification of gastric infections (i.e., ulcer, polyp, esophagitis, and bleeding) through wireless capsule endoscopy (WCE) ...

Classification of glomerular hypercellularity using convolutional features and support vector machine.

Glomeruli are histological structures of the kidney cortex formed by interwoven blood capillaries, and are responsible for blood filtration. Glomerula...

Machine learning detection of Atrial Fibrillation using wearable technology.

BACKGROUND: Atrial Fibrillation is the most common arrhythmia worldwide with a global age adjusted prevalence of 0.5% in 2010. Anticoagulation treatme...

First-in-human evaluation of a hand-held automated venipuncture device for rapid venous blood draws.

Obtaining venous access for blood sampling or intravenous (IV) fluid delivery is an essential first step in patient care. However, success rates rely ...

Multi-media biomarkers: Integrating information to improve lead exposure assessment.

Exposure assessment traditionally relies on biomarkers that measure chemical concentrations in individual biological media (i.e., blood, urine, etc.)....

A Deep Neural Network Application for Improved Prediction of [Formula: see text] in Type 1 Diabetes.

[Formula: see text] is a primary marker of long-term average blood glucose, which is an essential measure of successful control in type 1 diabetes. Pr...

Machine learning models for identifying preterm infants at risk of cerebral hemorrhage.

Intracerebral hemorrhage in preterm infants is a major cause of brain damage and cerebral palsy. The pathogenesis of cerebral hemorrhage is multifacto...

Expression of Cytokines and Chemokines as Predictors of Stroke Outcomes in Acute Ischemic Stroke.

Ischemic stroke remains one of the most debilitating diseases and is the fifth leading cause of death in the US. The ability to predict stroke outcom...

Predicting Chronic Subdural Hematoma Recurrence and Stroke Outcomes While Withholding Antiplatelet and Anticoagulant Agents.

The aging of the western population and the increased use of oral anticoagulation (OAC) and antiplatelet drugs (APD) will result in a clinical dilemm...

Clot Analog Attenuation in Non-contrast CT Predicts Histology: an Experimental Study Using Machine Learning.

Exact histological clot composition remains unknown. The purpose of this study was to identify the best imaging variables to be extrapolated on clot c...

Machine learning as new promising technique for selection of significant features in obese women with type 2 diabetes.

Background The global trend of obesity and diabetes is considerable. Recently, the early diagnosis and accurate prediction of type 2 diabetes mellitus...

Prediction of blood pressure variability using deep neural networks.

PURPOSE: The purpose of our study was to predict blood pressure variability from time-series data of blood pressure measured at home and data obtained...

Assessment of a Machine Learning Model Applied to Harmonized Electronic Health Record Data for the Prediction of Incident Atrial Fibrillation.

IMPORTANCE: Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia, and its early detection could lead to significant improvements i...

Automatic detection of blood content in capsule endoscopy images based on a deep convolutional neural network.

BACKGROUND AND AIM: Detecting blood content in the gastrointestinal tract is one of the crucial applications of capsule endoscopy (CE). The suspected ...

Effects of aged garlic extract on arterial elasticity in a placebo-controlled clinical trial using EndoPAT™ technology.

Cardiovascular diseases are the main cause of death in the industrialized world, with the main risk factors being elevated blood pressure and blood li...