Diabetes

Impaired left ventricular diastolic function in T2DM patients is closely related to glycemic control.

BACKGROUND: Left ventricular (LV) diastolic dysfunction commonly is observed in individuals with type 2 diabetes mellitus (T2DM). We employed transthoracic echocardiography (TTE) and cardiac magnetic resonance imaging (CMRI) to investigate the hypothesis that LV diastolic dysfunction in T2DM is associated with poor glycemic control.

Artificial neural network model for predicting the bioavailability of tacrolimus in patients with renal transplantation.

The objective of the current study was to explore the role of ABCB1 and CYP3A5 genetic polymorphisms in predicting the bioavailability of tacrolimus and the risk for post-transplant diabetes. Artificial neural network (ANN) and logistic regression (LR) models were used to predict the bioavailability of tacrolimus and risk for post-transplant diabetes, respectively. The five-fold cross-validation o...

A deep-learning classifier identifies patients with clinical heart failure using whole-slide images of H&E tissue.

Over 26 million people worldwide suffer from heart failure annually. When the cause of heart failure cannot be identified, endomyocardial biopsy (EMB)...

Pharmacological therapy selection of type 2 diabetes based on the SWARA and modified MULTIMOORA methods under a fuzzy environment.

Medication selection for Type 2 Diabetes (T2D) is a challenging medical decision-making problem involving multiple medications that can be prescribed ...

The Promise and Perils of Wearable Physiological Sensors for Diabetes Management.

Development of truly useful wearable physiologic monitoring devices for use in diabetes management is still in its infancy. From wearable activity mon...

Grader Variability and the Importance of Reference Standards for Evaluating Machine Learning Models for Diabetic Retinopathy.

PURPOSE: Use adjudication to quantify errors in diabetic retinopathy (DR) grading based on individual graders and majority decision, and to train an i...

Application of an artificial neural network model for diagnosing type 2 diabetes mellitus and determining the relative importance of risk factors.

OBJECTIVES: To identify the most important demographic risk factors for a diagnosis of type 2 diabetes mellitus (T2DM) using a neural network model.

Antiphospholipase 2 receptor antibody levels to predict complete spontaneous remission in primary membranous nephropathy.

BACKGROUND: M-type phospholipase A2 receptor (APLA2R) is considered the major antigen involved in the pathogenesis of adult primary membranous nephrop...

Efficacy of a Deep Learning System for Detecting Glaucomatous Optic Neuropathy Based on Color Fundus Photographs.

PURPOSE: To assess the performance of a deep learning algorithm for detecting referable glaucomatous optic neuropathy (GON) based on color fundus phot...

A machine learning approach for automated assessment of retinal vasculature in the oxygen induced retinopathy model.

Preclinical studies of vascular retinal diseases rely on the assessment of developmental dystrophies in the oxygen induced retinopathy rodent model. T...

Segmentation of Intra-Retinal Cysts From Optical Coherence Tomography Images Using a Fully Convolutional Neural Network Model.

Optical coherence tomography (OCT) is an imaging modality that is used extensively for ophthalmic diagnosis, near-histological visualization, and quan...

Personalized prediction of drug efficacy for diabetes treatment via patient-level sequential modeling with neural networks.

Patients with type 2 diabetes mellitus are generally under continuous long-term medical treatment based on anti-diabetic drugs to achieve the desired ...

Microaneurysm detection using fully convolutional neural networks.

BACKROUND AND OBJECTIVES: Diabetic retinopathy is a microvascular complication of diabetes that can lead to sight loss if treated not early enough. Mi...

Friendship with a robot: Children's perception of similarity between a robot's physical and virtual embodiment that supports diabetes self-management.

OBJECTIVE: The PAL project develops a conversational agent with a physical (robot) and virtual (avatar) embodiment to support diabetes self-management...

Combining deep residual neural network features with supervised machine learning algorithms to classify diverse food image datasets.

Obesity is increasing worldwide and can cause many chronic conditions such as type-2 diabetes, heart disease, sleep apnea, and some cancers. Monitorin...

DMTO: a realistic ontology for standard diabetes mellitus treatment.

BACKGROUND: Treatment of type 2 diabetes mellitus (T2DM) is a complex problem. A clinical decision support system (CDSS) based on massive and distribu...

Comparing Three Data Mining Algorithms for Identifying the Associated Risk Factors of Type 2 Diabetes.

BACKGROUND: Increasing the prevalence of type 2 diabetes has given rise to a global health burden and a concern among health service providers and hea...

Predicting non-melanoma skin cancer via a multi-parameterized artificial neural network.

Ultraviolet radiation (UVR) exposure and family history are major associated risk factors for the development of non-melanoma skin cancer (NMSC). The ...

Permutation Entropy and Signal Energy Increase the Accuracy of Neuropathic Change Detection in Needle EMG.

Needle electromyography can be used to detect the number of changes and morphological changes in motor unit potentials of patients with axonal neurop...

Machine learning techniques for medical diagnosis of diabetes using iris images.

BACKGROUND AND OBJECTIVE: Complementary and alternative medicine techniques have shown their potential for the treatment and diagnosis of chronical di...