Diabetes

[OCT biomarkers in diabetic maculopathy and artificial intelligence].

Diabetes mellitus is a chronic disease the microvascular complications of which include diabetic retinopathy and maculopathy. Diabetic macular edema, proliferative diabetic retinopathy, and diabetic macular ischemia pose a threat to visual acuity. Artificial intelligence can play an increasingly more important role in making the diagnosis and the treatment regimen of maculopathies in everyday clin...

Association between myosteatosis and impaired glucose metabolism: A deep learning whole-body magnetic resonance imaging population phenotyping approach.

BACKGROUND: There is increasing evidence that myosteatosis, which is currently not assessed in clinical routine, plays an important role in risk estimation in individuals with impaired glucose metabolism, as it is associated with the progression of insulin resistance. With advances in artificial intelligence, automated and accurate algorithms have become feasible to fill this gap.

A transparent machine learning algorithm uncovers HbA1c patterns associated with therapeutic inertia in patients with type 2 diabetes and failure of metformin monotherapy.

AIMS: This study aimed to identify and categorize the determinants influencing the intensification of therapy in Type 2 Diabetes (T2D) patients with s...

Development and validation of 10-year risk prediction models of cardiovascular disease in Chinese type 2 diabetes mellitus patients in primary care using interpretable machine learning-based methods.

AIM: To develop 10-year cardiovascular disease (CVD) risk prediction models in Chinese patients with type 2 diabetes mellitus (T2DM) managed in primar...

Machine learning-based prediction of diabetic patients using blood routine data.

Diabetes stands as one of the most prevalent chronic diseases globally. The conventional methods for diagnosing diabetes are frequently overlooked unt...

Enhancing Diabetes Management for Older Patients: The Potential Role of ChatGPT.

Artificial intelligence (AI) technology has the potential to improve diabetes education for older individuals with diabetes. However, as AI becomes in...

Screening for diabetic retinopathy with artificial intelligence: a real world evaluation.

AIM: Periodic screening for diabetic retinopathy (DR) is effective for preventing blindness. Artificial intelligence (AI) systems could be useful for ...

The impact of machine learning on the prediction of diabetic foot ulcers - A systematic review.

INTRODUCTION: Globally, diabetes mellitus poses a significant health challenge as well as the associated complications of diabetes, such as diabetic f...

Smell cancer by machine learning-assisted peptide/MXene bioelectronic array.

Non-invasive detection of tumors is of utmost importance to save lives. Nonetheless, identifying tumors through gas analysis is a challenging task. In...

Computational approaches for lead compound discovery in dipeptidyl peptidase-4 inhibition using machine learning and molecular dynamics techniques.

The prediction of possible lead compounds from already-known drugs that may present DPP-4 inhibition activity imply a advantage in the drug developmen...

Development of a machine learning-based model for the prediction and progression of diabetic kidney disease: A single centred retrospective study.

BACKGROUND: Diabetic kidney disease (DKD) is a diabetic microvascular complication often characterized by an unpredictable progression. Hence, early d...

A hybrid model for the detection of retinal disorders using artificial intelligence techniques.

The prevalence of vision impairment is increasing at an alarming rate. The goal of the study was to create an automated method that uses optical coher...

Unlocking Optimal Glycemic Interpretation: Redefining HbA1c Analysis in Female Patients With Diabetes and Iron-Deficiency Anemia Using Machine Learning Algorithms.

OBJECTIVE: In response to the nuanced glycemic challenges faced by women with iron deficiency anemia (IDA) associated with diabetes, this study uses a...

Random survival forest for predicting the combined effects of multiple physiological risk factors on all-cause mortality.

Understanding the combined effects of risk factors on all-cause mortality is crucial for implementing effective risk stratification and designing targ...

Estimating the prevalence of diabetic retinopathy in electronic health records with massive missing labels.

OBJECTIVE: The paper aims to address the problem of massive unlabeled patients in electronic health records (EHR) who potentially have undiagnosed dia...

Artificial intelligence derived categorizations significantly improve HOMA IR/β indicators: Combating diabetes through cross-interacting drugs.

Improvements in the homeostasis model assessment of insulin resistance (HOMA-IR) and homeostasis model assessment of beta-cell function (HOMA-β) signi...

Risk factors for prediabetes in community-dwelling adults: A generalized estimating equation logistic regression approach with natural language processing insights.

The global prevalence of prediabetes is expected to reach 8.3% (587 million people) by 2045, with 70% of people with prediabetes developing diabetes d...

An automatic deep reinforcement learning bolus calculator for automated insulin delivery systems.

In hybrid automatic insulin delivery (HAID) systems, meal disturbance is compensated by feedforward control, which requires the announcement of the me...

Identification of key biomarkers for early warning of diabetic retinopathy using BP neural network algorithm and hierarchical clustering analysis.

Diabetic retinopathy is one of the most common microangiopathy in diabetes, essentially caused by abnormal blood glucose metabolism resulting from ins...

A deep neural network prediction method for diabetes based on Kendall's correlation coefficient and attention mechanism.

Diabetes is a chronic disease, which is characterized by abnormally high blood sugar levels. It may affect various organs and tissues, and even lead t...