Endocrinology

Diabetes

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

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Showing 2332-2352 of 2,621 articles
Artificial intelligence for retinopathy of prematurity.

PURPOSE OF REVIEW: In this article, we review the current state of artificial intelligence applicati...

Gut Microbiota in T1DM-Onset Pediatric Patients: Machine-Learning Algorithms to Classify Microorganisms as Disease Linked.

AIMS: The purpose of this work is to find the gut microbial fingerprinting of pediatric patients wit...

Workforce Shortage for Retinopathy of Prematurity Care and Emerging Role of Telehealth and Artificial Intelligence.

Retinopathy of prematurity (ROP) is the leading cause of childhood blindness in very-low-birthweight...

Artificial Intelligence Uncovered Clinical Factors for Cardiovascular Events in Myocardial Infarction Patients with Glucose Intolerance.

PURPOSE: Glucose intolerance (GI), defined as either prediabetes or diabetes, promotes cardiovascula...

A Multi-Label Deep Learning Model with Interpretable Grad-CAM for Diabetic Retinopathy Classification.

The characteristics of diabetic retinopathy (DR) fundus images generally consist of multiple types o...

Predicting complications of diabetes mellitus using advanced machine learning algorithms.

OBJECTIVE: We sought to predict if patients with type 2 diabetes mellitus (DM2) would develop 10 sel...

The Personal Health Library: A Single Point of Secure Access to Patient Digital Health Information.

Traditionally, health data management has been EMR-based and mostly handled by health care providers...

Automatic Extraction of Risk Factors for Dialysis Patients from Clinical Notes Using Natural Language Processing Techniques.

Studies have shown that mental health and comorbidities such as dementia, diabetes and cardiovascula...

Artificial Intelligence and Ophthalmology.

Artificial intelligence is advancing rapidly and making its way into all areas of our lives. This re...

Machine learning reveals serum sphingolipids as cholesterol-independent biomarkers of coronary artery disease.

BACKGROUNDCeramides are sphingolipids that play causative roles in diabetes and heart disease, with ...

Multiple-Image Deep Learning Analysis for Neuropathy Detection in Corneal Nerve Images.

PURPOSE: Automated classification of corneal confocal images from healthy subjects and diabetic subj...

Medios- An offline, smartphone-based artificial intelligence algorithm for the diagnosis of diabetic retinopathy.

PURPOSE: An observational study to assess the sensitivity and specificity of the Medios smartphone-b...

Chatteringfree hybrid adaptive neuro-fuzzy inference system-particle swarm optimisation data fusion-based BG-level control.

In this study, a closed-loop control scheme is proposed for the glucose-insulin regulatory system in...

Application of Artificial Intelligence in Targeting Retinal Diseases.

Retinal diseases affect an increasing number of patients worldwide because of the aging population. ...

Artificial Intelligence for Cataract Detection and Management.

The rising popularity of artificial intelligence (AI) in ophthalmology is fuelled by the ever-increa...

Artificial Intelligence in Ophthalmology: Evolutions in Asia.

Artificial intelligence (AI) has been studied in ophthalmology since availability of digital informa...

Developing a Prototype Knowledge-Based System for Diagnosis and Treatment of Diabetes Using Data Mining Techniques.

BACKGROUND: Diabetes is a disease that affects the body's ability to produce or use insulin. A total...

Deep Learning Techniques for Diabetic Retinopathy Detection.

Diabetes occurs due to the excess of glucose in the blood that may affect many organs of the body. E...

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