Diabetes

Elastic Deformation of Optical Coherence Tomography Images of Diabetic Macular Edema for Deep-Learning Models Training: How Far to Go?

UNLABELLED: - Objective: To explore the clinical validity of elastic deformation of optical coherence tomography (OCT) images for data augmentation in the development of deep-learning model for detection of diabetic macular edema (DME).

Prediction of gestational diabetes mellitus at the first trimester: machine-learning algorithms.

PURPOSE: Short- and long-term complications of gestational diabetes mellitus (GDM) involving pregnancies and offspring warrant the development of an effective individualized risk prediction model to reduce and prevent GDM together with its associated co-morbidities. The aim is to use machine learning (ML) algorithms to study data gathered throughout the first trimester in order to predict GDM.

High-precision prediction of blood glucose concentration utilizing Fourier transform Raman spectroscopy and an ensemble machine learning algorithm.

Raman spectroscopy has gained popularity in analyzing blood glucose levels due to its non-invasive identification and minimal interference from water....

Attention-based deep learning framework to recognize diabetes disease from cellular retinal images.

A medical disorder known as diabetic retinopathy (DR) affects people who suffer from diabetes. Many people are visually impaired due to DR. Primary ca...

Explainable artificial intelligence on life satisfaction, diabetes mellitus and its comorbid condition.

This study uses artificial intelligence for testing (1) whether the comorbidity of diabetes and its comorbid condition is very strong in the middle-ag...

URNet: System for recommending referrals for community screening of diabetic retinopathy based on deep learning.

Diabetic retinopathy (DR) will cause blindness if the detection and treatment are not carried out in the early stages. To create an effective treatmen...

Exploring the potential of FDA approved anti-diabetic drugs for repurposing against COVID-19: a core combination of multiple computational strategies and integrated artificial intelligence.

The latest variant of coronavirus is omicron. The World Health Organization (WHO) designated variation 'B.1.1.529' named omicron as a variant of conce...

Stratification of diabetes in the context of comorbidities, using representation learning and topological data analysis.

Diabetes is a heterogenous, multimorbid disorder with a large variation in manifestations, trajectories, and outcomes. The aim of this study is to val...

Opportunistic detection of type 2 diabetes using deep learning from frontal chest radiographs.

Deep learning (DL) models can harness electronic health records (EHRs) to predict diseases and extract radiologic findings for diagnosis. With ambulat...

Glucose-6-phosphate dehydrogenase deficiency with coinherited Gaucher disease: A rare association.

Anemia coexisting with Gaucher disease (GD) is often associated with non-hemolytic processes. Few cases of GD with autoimmune hemolytic anemia have be...

Predicting GPR40 Agonists with A Deep Learning-Based Ensemble Model.

Recent studies have identified G protein-coupled receptor 40 (GPR40) as a promising target for treating type 2 diabetes mellitus, and GPR40 agonists h...

A Deep Learning Regression Model for Photonic Crystal Fiber Sensor With XAI Feature Selection and Analysis.

A Deep Learning Multi-output regression model is employed to correctly model the relationships between optical design parameters of an asymmetric Twin...

Artificial intelligence in ophthalmology: The path to the real-world clinic.

Artificial intelligence (AI) has great potential to transform healthcare by enhancing the workflow and productivity of clinicians, enabling existing s...

A novel missing data imputation approach based on clinical conditional Generative Adversarial Networks applied to EHR datasets.

The missing data mechanism is a relevant problem in Machine Learning (ML) and biomedical informatics communities. Real-world Electronic Health Record ...

Using Deep Learning Architectures for Detection and Classification of Diabetic Retinopathy.

Diabetic retinopathy (DR) is a common complication of long-term diabetes, affecting the human eye and potentially leading to permanent blindness. The ...

External validation of a deep learning detection system for glaucomatous optic neuropathy: a real-world multicentre study.

OBJECTIVES: To conduct an external validation of an automated artificial intelligence (AI) diagnostic system using fundus photographs from a real-life...

Risk predictions of hospital-acquired pressure injury in the intensive care unit based on a machine learning algorithm.

Pressure injury (PI), or local damage to soft tissues and skin caused by prolonged pressure, remains controversial in the medical world. Patients in i...

Discovery of senolytics using machine learning.

Cellular senescence is a stress response involved in ageing and diverse disease processes including cancer, type-2 diabetes, osteoarthritis and viral ...

Deep learning for automated detection of neovascular leakage on ultra-widefield fluorescein angiography in diabetic retinopathy.

Diabetic retinopathy is a leading cause of blindness in working-age adults worldwide. Neovascular leakage on fluorescein angiography indicates progres...

A diabetes prediction model based on Boruta feature selection and ensemble learning.

BACKGROUND AND OBJECTIVE: As a common chronic disease, diabetes is called the "second killer" among modern diseases. Currently, there is no medical cu...