Diabetes

Detection of Diabetic Eye Disease from Retinal Images Using a Deep Learning Based CenterNet Model.

Diabetic retinopathy (DR) is an eye disease that alters the blood vessels of a person suffering from diabetes. Diabetic macular edema (DME) occurs when DR affects the macula, which causes fluid accumulation in the macula. Efficient screening systems require experts to manually analyze images to recognize diseases. However, due to the challenging nature of the screening method and lack of trained h...

DeepUWF: An Automated Ultra-Wide-Field Fundus Screening System via Deep Learning.

The emerging ultra-wide field of view (UWF) fundus color imaging is a powerful tool for fundus screening. However, manual screening is labor-intensive and subjective. Based on 2644 UWF images, a set of early fundus abnormal screening system named DeepUWF is developed. DeepUWF includes an abnormal fundus screening subsystem and a disease diagnosis subsystem for three kinds of fundus diseases (retin...

Real-world artificial intelligence-based opportunistic screening for diabetic retinopathy in endocrinology and indigenous healthcare settings in Australia.

This study investigated the diagnostic performance, feasibility, and end-user experiences of an artificial intelligence (AI)-assisted diabetic retinop...

Development and validation of a new diabetes index for the risk classification of present and new-onset diabetes: multicohort study.

In this study, we aimed to propose a novel diabetes index for the risk classification based on machine learning techniques with a high accuracy for di...

CAFT: a deep learning-based comprehensive abdominal fat analysis tool for large cohort studies.

BACKGROUND: There is increasing appreciation of the association of obesity beyond co-morbidities, such as cancers, Type 2 diabetes, hypertension, and ...

A New Clinical Utility for Tubular Markers to Identify Kidney Responders to Saxagliptin Treatment in Adults With Diabetic Nephropathy.

OBJECTIVES: In recent clinical studies, saxagliptin exhibited nephroprotective potential by lowering albuminuria. In this study, we aimed to determine...

Multimodal Machine Learning Using Visual Fields and Peripapillary Circular OCT Scans in Detection of Glaucomatous Optic Neuropathy.

PURPOSE: To develop and validate a multimodal artificial intelligence algorithm, FusionNet, using the pattern deviation probability plots from visual ...

Characterization of Ultra-Widefield Angiographic Vascular Features in Diabetic Retinopathy with Automated Severity Classification.

PURPOSE: To determine the association between diabetic retinopathy (DR) severity and quantitative retinal vascular features.

Five regions, five retinopathy screening programmes: a systematic review of how Portugal addresses the challenge.

BACKGROUND: The implementation of a population-based screening programme for diabetic retinopathy involves several challenges, often leading to postpo...

A Multitask Deep-Learning System to Classify Diabetic Macular Edema for Different Optical Coherence Tomography Devices: A Multicenter Analysis.

OBJECTIVE: Diabetic macular edema (DME) is the primary cause of vision loss among individuals with diabetes mellitus (DM). We developed, validated, an...

Deep Learning for Diabetes: A Systematic Review.

Diabetes is a chronic metabolic disorder that affects an estimated 463 million people worldwide. Aiming to improve the treatment of people with diabet...

Diagnosis of retinal disorders from Optical Coherence Tomography images using CNN.

An efficient automatic decision support system for detection of retinal disorders is important and is the need of the hour. Optical Coherence Tomograp...

Learning Carbohydrate Digestion and Insulin Absorption Curves Using Blood Glucose Level Prediction and Deep Learning Models.

Type 1 diabetes is a chronic disease caused by the inability of the pancreas to produce insulin. Patients suffering type 1 diabetes depend on the appr...

Generalizability of heterogeneous treatment effects based on causal forests applied to two randomized clinical trials of intensive glycemic control.

Purpose Machine learning is an attractive tool for identifying heterogeneous treatment effects (HTE) of interventions but generalizability of machine ...

Economic Evaluations of Artificial Intelligence in Ophthalmology.

Artificial intelligence (AI) is expected to cause significant medical quality enhancements and cost-saving improvements in ophthalmology. Although the...

Ocular blood flow as it relates to race and disease on glaucoma.

Glaucoma is a multifactorial progressive and degenerative optic neuropathy representing one of the world's leading cause of irreversible blindness. Cu...

An interpretable multiple-instance approach for the detection of referable diabetic retinopathy in fundus images.

Diabetic retinopathy (DR) is one of the leading causes of vision loss across the world. Yet despite its wide prevalence, the majority of affected peop...

Assistive Framework for Automatic Detection of All the Zones in Retinopathy of Prematurity Using Deep Learning.

Retinopathy of prematurity (ROP) is a potentially blinding disorder seen in low birth weight preterm infants. In India, the burden of ROP is high, wit...

Deep learning-based automated detection for diabetic retinopathy and diabetic macular oedema in retinal fundus photographs.

OBJECTIVES: To present and validate a deep ensemble algorithm to detect diabetic retinopathy (DR) and diabetic macular oedema (DMO) using retinal fund...

Machine Learning for Predicting the 3-Year Risk of Incident Diabetes in Chinese Adults.

We aimed to establish and validate a risk assessment system that combines demographic and clinical variables to predict the 3-year risk of incident d...