Diabetes

Retinopathy Analysis Based on Deep Convolution Neural Network.

At medical checkups or mass screenings, the fundus examination is effective for early detection of s...

Deep Transfer Learning Models for Medical Diabetic Retinopathy Detection.

INTRODUCTION: Diabetic retinopathy (DR) is the most common diabetic eye disease worldwide and a lead...

[Stage Ⅳ Breast Cancer with Difficulty in Initiating Chemotherapy-A Case Report].

We report a case of breast cancer(T4b[skin], N1, M1[lung], ER-, PR-, HER2 3+)in a 63-year-old woman ...

Sodium glucose co-transporter 2 inhibitors and cardiovascular event protections: how applicable are clinical trials and observational studies to real-world patients?

OBJECTIVE: This study evaluated the characteristics of new users of sodium glucose co-transporter 2 ...

Maternal serum pentraxin 3 level in early pregnancy for prediction of gestational diabetes mellitus.

BACKGROUND: Our study aimed to reveal the relationship of maternal pentraxin 3 (PTX3)'s serum concen...

Development and Validation of a Deep Learning System to Detect Glaucomatous Optic Neuropathy Using Fundus Photographs.

IMPORTANCE: A deep learning system (DLS) that could automatically detect glaucomatous optic neuropat...

Evaluation of a Deep Learning System For Identifying Glaucomatous Optic Neuropathy Based on Color Fundus Photographs.

PRECIS: Pegasus outperformed 5 of the 6 ophthalmologists in terms of diagnostic performance, and the...

OPTICAL COHERENCE TOMOGRAPHY BIOMARKERS TO DISTINGUISH DIABETIC MACULAR EDEMA FROM PSEUDOPHAKIC CYSTOID MACULAR EDEMA USING MACHINE LEARNING ALGORITHMS.

PURPOSE: In diabetic patients presenting with macular edema (ME) shortly after cataract surgery, ide...

Relevant Features in Nonalcoholic Steatohepatitis Determined Using Machine Learning for Feature Selection.

We investigated the prevalence and the most relevant features of nonalcoholic steatohepatitis (NASH...

Convolutional neural networks for wound detection: the role of artificial intelligence in wound care.

OBJECTIVE: Telemedicine is an essential support system for clinical settings outside the hospital. R...

A new and efficient numerical method for the fractional modeling and optimal control of diabetes and tuberculosis co-existence.

The main objective of this research is to investigate a new fractional mathematical model involving ...

Identifying Diabetes in Clinical Notes in Hebrew: A Novel Text Classification Approach Based on Word Embedding.

NimbleMiner is a word embedding-based, language-agnostic natural language processing system for clin...

Automatic Parallel Detection of Neovascularization from Retinal Images Using Ensemble of Extreme Learning Machine.

Retinopathy screening is a non-invasive method to collect retinal images and neovascularization dete...

Homogeneous and heterogeneous ensemble classification methods in diabetes disease: a review.

This paper explores the use of ensemble classification methods in the context of the diabetes diseas...

An Interpretable Ensemble Deep Learning Model for Diabetic Retinopathy Disease Classification.

Diabetic retinopathy (DR) is one kind of eye disease that is caused by overtime diabetes. Lots of pa...

Artificial intelligence in diabetic retinopathy: A natural step to the future.

Use of artificial intelligence in medicine in an evolving technology which holds promise for mass sc...

Use of Artificial Intelligence to Improve Diabetes Outcomes in Patients Using Multiple Daily Injections Therapy.

Using Machine Learning Applied to Real-World Healthcare Data for Predictive Analytics: An Applied Example in Bariatric Surgery.

OBJECTIVES: Laparoscopic metabolic surgery (MxS) can lead to remission of type 2 diabetes (T2D); how...

An outcome model approach to transporting a randomized controlled trial results to a target population.

OBJECTIVE: Participants enrolled into randomized controlled trials (RCTs) often do not reflect real-...

Continuous Glucose Monitoring Linked to an Artificial Intelligence Risk Index: Early Footprints of Intraventricular Hemorrhage in Preterm Neonates.

OBJECTIVE: To develop and validate a new risk score for intraventricular hemorrhage (IVH) in preterm...

Deep Learning Predicts OCT Measures of Diabetic Macular Thickening From Color Fundus Photographs.

PURPOSE: To develop deep learning (DL) models for the automatic detection of optical coherence tomog...