Diabetes

Predicting hypoglycemia in ICU patients: a machine learning approach.

BACKGROUND: The current study sets out to develop and validate a robust machine-learning model utilizing electronic health records (EHR) to forecast the risk of hypoglycemia among ICU patients in Jordan.

An attentional mechanism model for segmenting multiple lesion regions in the diabetic retina.

Diabetic retinopathy (DR), a leading cause of blindness in diabetic patients, necessitates the precise segmentation of lesions for the effective grading of lesions. DR multi-lesion segmentation faces the main concerns as follows. On the one hand, retinal lesions vary in location, shape, and size. On the other hand, the currently available multi-lesion region segmentation models are insufficient in...

Beneficial effects of cranberry juice enriched with omega-3 fatty acids in patients with type 2 diabetic and periodontal disease: A randomized pilot clinical trial.

BACKGROUND: The present study evaluated the effects of cranberry juice enriched with omega-3 on inflammatory, oxidative stress, and periodontal status...

Novel artificial intelligence for diabetic retinopathy and diabetic macular edema: what is new in 2024?

PURPOSE OF REVIEW: Given the increasing global burden of diabetic retinopathy and the rapid advancements in artificial intelligence, this review aims ...

The utility of a machine learning model in identifying people at high risk of type 2 diabetes mellitus.

BACKGROUND: According to previous reports, very high percentages of individuals in Saudi Arabia are undiagnosed for type 2 diabetes mellitus (T2DM). D...

Evaluation of AI-enhanced non-mydriatic fundus photography for diabetic retinopathy screening.

OBJECTIVE: To assess the feasibility of using non-mydriatic fundus photography in conjunction with an artificial intelligence (AI) reading platform fo...

Development and validation of a machine learning-based approach to identify high-risk diabetic cardiomyopathy phenotype.

AIMS: Abnormalities in specific echocardiographic parameters and cardiac biomarkers have been reported among individuals with diabetes. However, a com...

Integrating Laser-Induced Breakdown Spectroscopy and Ensemble Learning as Minimally Invasive Optical Screening for Diabetes.

Diabetes mellitus is a prevalent chronic disease necessitating timely identification for effective management. This paper introduces a reliable, strai...

Comparison review of image classification techniques for early diagnosis of diabetic retinopathy.

Diabetic retinopathy (DR) is one of the leading causes of vision loss in adults and is one of the detrimental side effects of the mass prevalence of D...

Long-term Major Adverse Cardiac Event Prediction by Computed Tomography-derived Plaque Measures and Clinical Parameters Using Machine Learning.

Objective The present study evaluated the usefulness of machine learning (ML) models with the coronary computed tomography imaging and clinical parame...

Comparison of Machine Learning Algorithms and Nomogram Construction for Diabetic Retinopathy Prediction in Type 2 Diabetes Mellitus Patients.

INTRODUCTION: The aim of this study was to compare various machine learning algorithms for constructing a diabetic retinopathy (DR) prediction model a...

Deep-Learning-Based Blood Glucose Detection Device Using Acetone Exhaled Breath Sensing Features of α-FeO-MWCNT Nanocomposites.

Owing to the correlation between acetone in human's exhaled breath (EB) and blood glucose, the development of EB acetone gas-sensing devices is import...

Fully automated epicardial adipose tissue volume quantification with deep learning and relationship with CAC score and micro/macrovascular complications in people living with type 2 diabetes: the multicenter EPIDIAB study.

BACKGROUND: The aim of this study (EPIDIAB) was to assess the relationship between epicardial adipose tissue (EAT) and the micro and macrovascular com...

Ethical Considerations in the Design and Conduct of Clinical Trials of Artificial Intelligence.

IMPORTANCE: Safe integration of artificial intelligence (AI) into clinical settings often requires randomized clinical trials (RCT) to compare AI effi...

SCINet: A Segmentation and Classification Interaction CNN Method for Arteriosclerotic Retinopathy Grading.

As a common disease, cardiovascular and cerebrovascular diseases pose a great harm threat to human wellness. Even using advanced and comprehensive tre...

Construction of Risk Prediction Model of Type 2 Diabetic Kidney Disease Based on Deep Learning (Diabetes Metab J 2024;48:771-9).

Construction of Risk Prediction Model of Type 2 Diabetic Kidney Disease Based on Deep Learning (Diabetes Metab J 2024;48:771-9).

The early prediction of gestational diabetes mellitus by machine learning models.

BACKGROUND: We aimed to determine the best-performing machine learning (ML)-based algorithm for predicting gestational diabetes mellitus (GDM) with so...

Deciphering Dormant Cells of Lung Adenocarcinoma: Prognostic Insights from O-glycosylation-Related Tumor Dormancy Genes Using Machine Learning.

Lung adenocarcinoma (LUAD) poses significant challenges due to its complex biological characteristics and high recurrence rate. The high recurrence ra...

Federated Learning in Glaucoma: A Comprehensive Review and Future Perspectives.

CLINICAL RELEVANCE: Glaucoma is a complex eye condition with varied morphological and clinical presentations, making diagnosis and management challeng...