Endocrinology

Diabetes

Latest AI and machine learning research in diabetes for healthcare professionals.

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Showing 358-378 of 2,574 articles
Deep Ensemble for Central Serous Microscopic Retinopathy Detection in Retinal Optical Coherence Tomographic Images.

The retina is an important part of the eye that aids in focusing light and visual recognition to the...

Is personality associated with the lived experience of the NHS England low calorie diet programme: A pilot study.

This pilot study explored the use of a novel behavioural artificial intelligence (AI) tool to examin...

The Central Role of Learning in Preventing Foot Complications in Persons With Diabetes: A Scoping Review.

BACKGROUND: Despite a variety of literature reviews, there is limited understanding of the learning ...

Detecting severe coronary artery stenosis in T2DM patients with NAFLD using cardiac fat radiomics-based machine learning.

To analyze radiomics features of cardiac adipose tissue in individuals with type 2 diabetes (T2DM) a...

A feature explainability-based deep learning technique for diabetic foot ulcer identification.

Diabetic foot ulcers (DFUs) are a common and serious complication of diabetes, presenting as open so...

Early gestational diabetes mellitus risk predictor using neural network with NearMiss.

BACKGROUND: Gestational diabetes mellitus (GDM) is globally recognized as a significant pregnancy-re...

MSTNet: Multi-scale spatial-aware transformer with multi-instance learning for diabetic retinopathy classification.

Diabetic retinopathy (DR), the leading cause of vision loss among diabetic adults worldwide, undersc...

Development and validation of predictive models for diabetic retinopathy using machine learning.

OBJECTIVE: This study aimed to develop and compare machine learning models for predicting diabetic r...

Neural Network-Enhanced Electrochemical/SERS Dual-Mode Microfluidic Platform for Accurate Detection of Interleukin-6 in Diabetic Wound Exudates.

Interleukin-6 (IL-6) plays a pivotal role in the inflammatory response of diabetic wounds, providing...

Food-derived DPP4 inhibitors: Drug discovery based on high-throughput virtual screening and deep learning.

Dipeptidyl peptidase-4 (DPP-4) is a critical target for the treatment of type 2 diabetes. This study...

Disease diagnostics using machine learning of B cell and T cell receptor sequences.

Clinical diagnosis typically incorporates physical examination, patient history, various laboratory ...

Diagnostic Accuracy of IDX-DR for Detecting Diabetic Retinopathy: A Systematic Review and Meta-Analysis.

PURPOSE: Diabetic retinopathy (DR) is a leading cause of vision loss worldwide, making early detecti...

Use of a Convolutional Neural Network to Predict the Response of Diabetic Macular Edema to Intravitreal Anti-VEGF Treatment: A Pilot Study.

PURPOSE: To utilize a convolutional neural network (CNN) to predict the response of treatment-naïve ...

Reinforcement-based leveraging transfer learning for multiclass optical coherence tomography images classification.

The accurate diagnosis of retinal diseases, such as Diabetic Macular Edema (DME) and Age-related Mac...

Building an intelligent diabetes Q&A system with knowledge graphs and large language models.

INTRODUCTION: This paper introduces an intelligent question-answering system designed to deliver per...

CT-Based Machine Learning Radiomics Analysis to Diagnose Dysthyroid Optic Neuropathy.

PURPOSE: To develop CT-based machine learning radiomics models used for the diagnosis of dysthyroid ...

Diabetic peripheral neuropathy detection of type 2 diabetes using machine learning from TCM features: a cross-sectional study.

AIMS: Diabetic peripheral neuropathy (DPN) is the most common complication of diabetes mellitus. Ear...

Leveraging OGTT derived metabolic features to detect Binge-eating disorder in individuals with high weight: a "seek out" machine learning approach.

Binge eating disorder (BED) carries a 6 times higher risk for obesity and accounts for roughly 30% o...

Enhancing diabetic retinopathy diagnosis: automatic segmentation of hyperreflective foci in OCT via deep learning.

OBJECTIVE: Hyperreflective foci (HRF) are small, punctate lesions ranging from 20 to 50 m and exhib...

Capsule network-based deep learning for early and accurate diabetic retinopathy detection.

Glaucoma, an optic nerve disease resulting in blindness if left untreated, is a difficult condition ...

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