Diabetes

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) and non-alcoholic fatty liver disease (NAFLD), integrating relevant clinical indicators, and employing machine learning techniques to construct a precise model for detecting severe coronary artery stenosis. A retrospective analysis of 710 T2DM patients with NAFLD was conducted at First People's Hospi...

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

BACKGROUND: Gestational diabetes mellitus (GDM) is globally recognized as a significant pregnancy-related condition, contributing to complex complications for both mothers and infants. Traditional glucose tolerance tests lack the ability to identify the risk of GDM in early pregnancy, hindering effective prevention and timely intervention during the initial stages.

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, underscores the importance of early detection and timely ...

SERPINA3: A Novel Therapeutic Target for Diabetes-Related Cognitive Impairment Identified Through Integrated Machine Learning and Molecular Docking Analysis.

Diabetes-related cognitive impairment (DCI) is a severe complication of type 2 diabetes mellitus (T2DM), with limited understanding of its molecular m...

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 retinopathy (DR) using clinical and biochemical dat...

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 critical insights for clinicians in the developme...

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 outlines the development of six compounds derived...

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

Clinical diagnosis typically incorporates physical examination, patient history, various laboratory tests, and imaging studies but makes limited use o...

Plantar Thermogram Analysis Using Deep Learning for Diabetic Foot Risk Classification.

AIMS: Thermography is a noninvasive method to identify patients at risk of diabetic foot ulcers. In this study, we employed thermography and deep lear...

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 diabetic macular edema (DME) to a single injection...

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 detection critical to prevent blindness. IDX-DR, an FDA-a...

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 Macular Degeneration (AMD), is essential for preventi...

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 personalized medical information to diabetic patients...

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 optic neuropathy (DON).

Predicting diabetes self-management education engagement: machine learning algorithms and models.

INTRODUCTION: Diabetes self-management education (DSME) is endorsed by the American Diabetes Association (ADA) as an essential component of diabetes m...

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 in healthcare in view of its diagnostic difficulti...

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 exhibiting high reflective intensity in optical coheren...

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% of type 2 diabetes cases. Timely identification of ...

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. Early identification of individuals at high risk of D...

Artificial Intelligence for Diabetic Foot Screening Based on Digital Image Analysis: A Systematic Review.

INTRODUCTION: Early detection of diabetic foot complications is essential for effective management and prevention of complications. Artificial intelli...