Diabetes

Appropriateness of Artificial Intelligence Chatbots in Diabetic Foot Ulcer Management: Reply.

In response to the commentary by Daungsupawong and Wiwanitkit (doi: 10.1177/15347346241247914), we a...

Correlation of limited-early-response status with 12-month CST, BVA, and machine learning-quantified retinal fluid in diabetic macular oedema in routine clinical practice.

BACKGROUND/OBJECTIVES: Anti-VEGF treatment response in DMO has been measured by changes in the centr...

Assessing spectral effectiveness in color fundus photography for deep learning classification of retinopathy of prematurity.

SIGNIFICANCE: Retinopathy of prematurity (ROP) poses a significant global threat to childhood vision...

Diabetic retinopathy screening through artificial intelligence algorithms: A systematic review.

Diabetic retinopathy (DR) poses a significant challenge in diabetes management, with its progression...

Association between machine learning-assisted heavy metal exposures and diabetic kidney disease: a cross-sectional survey and Mendelian randomization analysis.

BACKGROUND AND OBJECTIVE: Heavy metals, ubiquitous in the environment, pose a global public health c...

Lipids balance as a spectroscopy marker of diabetes. Analysis of FTIR spectra by 2D correlation and machine learning analyses.

The number of people suffering from type 2 diabetes has rapidly increased. Taking into account, that...

Identification of diabetic retinopathy classification using machine learning algorithms on clinical data and optical coherence tomography angiography.

BACKGROUND: To apply machine learning (ML) algorithms to perform multiclass diabetic retinopathy (DR...

Assessing the Utility, Impact, and Adoption Challenges of an Artificial Intelligence-Enabled Prescription Advisory Tool for Type 2 Diabetes Management: Qualitative Study.

BACKGROUND: The clinical management of type 2 diabetes mellitus (T2DM) presents a significant challe...

Dynamic Individualized Risk Prediction in IgA Nephropathy: Entering the Era of Artificial Intelligence.

Use of an Artificial Intelligence-Generated Vascular Severity Score Improved Plus Disease Diagnosis in Retinopathy of Prematurity.

PURPOSE: To evaluate whether providing clinicians with an artificial intelligence (AI)-based vascula...

Artificial Intelligence Applications in Diabetic Retinopathy: What We Have Now and What to Expect in the Future.

Diabetic retinopathy (DR) is a major complication of diabetes mellitus and is a leading cause of vis...

Identification of novel biomarkers to distinguish clear cell and non-clear cell renal cell carcinoma using bioinformatics and machine learning.

Renal cell carcinoma (RCC), accounting for 90% of all kidney cancer, is categorized into clear cell ...

Interpretable machine learning identifies metabolites associated with glomerular filtration rate in type 2 diabetes patients.

OBJECTIVE: The co-occurrence of kidney disease in patients with type 2 diabetes (T2D) is a major pub...

Apoptosis and NETotic cell death affect diabetic nephropathy independently: An study integrative study encompassing bioinformatics, machine learning, and experimental validation.

OBJECTIVE: Although programmed cell death (PCD) and diabetic nephropathy (DN) are intrinsically conn...

Exploring the relationship between heavy metals and diabetic retinopathy: a machine learning modeling approach.

Diabetic retinopathy (DR) is one of the leading causes of adult blindness in the United States. Alth...

Identifying subtypes of type 2 diabetes mellitus with machine learning: development, internal validation, prognostic validation and medication burden in linked electronic health records in 420 448 individuals.

INTRODUCTION: None of the studies of type 2 diabetes (T2D) subtyping to date have used linked popula...

Prediction of pharmaceutical and non-pharmaceutical expenditures associated with Diabetes Mellitus type II based on clinical risk.

OBJECTIVE: To assess the effectiveness of different machine learning models in estimating the pharma...

Au nanozyme-based colorimetric sensor array integrates machine learning to identify and discriminate monosaccharides.

As different monosaccharides exhibit different redox characteristics, this paper presented a novel c...

Integrated biomarker profiling for predicting the response of type 2 diabetes to metformin.

AIM: To explore biomarkers that can predict the response of type 2 diabetes (T2D) patients to metfor...

Utilizing machine learning algorithms for precise discrimination of glycosuria in fluorescence spectroscopic data.

Fluorescence spectroscopy coupled with a random forest machine learning algorithm offers a promising...

Explainable hypoglycemia prediction models through dynamic structured grammatical evolution.

Effective blood glucose management is crucial for people with diabetes to avoid acute complications....