Diabetes

Early prediction of cardiovascular events following treatments in female breast cancer patients: Application of real-world data and artificial intelligence.

• Application of real-world data and artificial intelligence in detecting cardiotoxicity following cancer treatment. • Clinical features have been used to develop prediction models. • Important features include age, tumor size, hypertension, HbA1c, HDL, creatinine, bilirubin, BUN, ALT, and diabetes. • This study offers potential approaches for cardio-oncology clinical practice.

Post-Bariatric Hypoglycemia After Gastric Bypass: Clinical Characteristics, Risk Factors, and Future Directions-A Response to Grover et al.

BACKGROUND: Post-bariatric hypoglycemia (PBH) after Roux-en-Y gastric bypass (RYGB) is a complex complication, often characterized by potentially severe hypoglycemic episodes and reduced hypoglycemia awareness. Recent findings suggest that autonomic dysfunction, preoperative hypoglycemia symptoms, and early dumping syndrome may each contribute to PBH risk.

Machine Learning Models Integrating Dietary Indicators Improve the Prediction of Progression from Prediabetes to Type 2 Diabetes Mellitus.

: Diet plays an important role in preventing and managing the progression from prediabetes to type 2 diabetes mellitus (T2DM). This study aims to deve...

Outcomes of lateral femoral cutaneous nerve decompression surgery in meralgia paraesthetica: assessment of pain, sensory deficits, and quality of life.

PURPOSE: Meralgia paraesthetica (MP) is a rare neuropathy of the lateral femoral cutaneous nerve (LFCN), characterized by thigh pain, paraesthesia, or...

Early prediction of postpartum dyslipidemia in gestational diabetes using machine learning models.

This study addresses a gap in research on predictive models for postpartum dyslipidemia in women with gestational diabetes mellitus (GDM). The goal wa...

Machine-learning assisted discovery unveils novel interplay between gut microbiota and host metabolic disturbance in diabetic kidney disease.

Diabetic kidney disease (DKD) is a serious healthcare dilemma. Nonetheless, the interplay between the functional capacity of gut microbiota and their ...

D-GET: Group-Enhanced Transformer for Diabetic Retinopathy Severity Classification in Fundus Fluorescein Angiography.

Early detection of Diabetic Retinopathy (DR) is vital for preserving vision and preventing deterioration of eyesight. Fundus Fluorescein Angiography (...

Circulating CCN6/WISP3 in type 2 diabetes mellitus patients and its correlation with insulin resistance and inflammation: statistical and machine learning analyses.

INTRODUCTION: Cellular Communication Network Factor 6 (CCN6) is an adipokine whose production undergoes significant alterations in metabolic disorders...

Retinal Vascularization Rate Predicts Retinopathy of Prematurity and Remains Unaffected by Low-Dose Bevacizumab Treatment.

PURPOSE: To assess the rate of retinal vascularization derived from ultra-widefield (UWF) imaging-based retinopathy of prematurity (ROP) screening as ...

Two-step pragmatic subgroup discovery for heterogeneous treatment effects analyses: perspectives toward enhanced interpretability.

Effect heterogeneity analyses using causal machine learning algorithms have gained popularity in recent years. However, the interpretation of estimate...

Investigation and validation of genes associated with endoplasmic reticulum stress in diabetic retinopathy using various machine learning algorithms.

BACKGROUND: Diabetic retinopathy (DR) is a common complication of diabetes, with Endoplasmic reticulum stress (ERS) playing a key role in cellular ada...

Performance of a Deep Learning Diabetic Retinopathy Algorithm in India.

IMPORTANCE: While prospective studies have investigated the accuracy of artificial intelligence (AI) for detection of diabetic retinopathy (DR) and di...

Machine learning-based risk predictive models for diabetic kidney disease in type 2 diabetes mellitus patients: a systematic review and meta-analysis.

BACKGROUND: Machine learning (ML) models are being increasingly employed to predict the risk of developing and progressing diabetic kidney disease (DK...

Artificial intelligence based predictive tools for identifying type 2 diabetes patients at high risk of treatment Non-adherence: A systematic review.

AIMS: Several Artificial Intelligence (AI) based predictive tools have been developed to predict non-adherence among patients with type 2 diabetes (T2...

Machine Learning-Driven D-Glucose Prediction Using a Novel Biosensor for Non-Invasive Diabetes Management.

Developing reliable noninvasive diagnostic and monitoring systems for diabetes remains a significant challenge, especially in the e-healthcare domain,...

Enhancing Ophthalmic Diagnosis and Treatment with Artificial Intelligence.

The integration of artificial intelligence (AI) in ophthalmology is transforming the field, offering new opportunities to enhance diagnostic accuracy,...

Deep Ensemble for Central Serous Microscopic Retinopathy Detection in Retinal Optical Coherence Tomographic Images.

The retina is an important part of the eye that aids in focusing light and visual recognition to the brain. Hence, its damage causes vision loss in th...

Toward Detection of Nocturnal Hypoglycemia in People With Diabetes Using Consumer-Grade Smartwatches and a Machine Learning Approach.

BACKGROUND: Nocturnal hypoglycemia poses significant risks to individuals with insulin-treated diabetes, impacting health and quality of life. Althoug...

Hypoglycemia Prediction in Type 1 Diabetes With Electrocardiography Beat Ensembles.

INTRODUCTION: Current methods to detect hypoglycemia in type 1 diabetes (T1D) require invasive sensors (ie, continuous glucose monitors, CGMs) that ge...

A feature explainability-based deep learning technique for diabetic foot ulcer identification.

Diabetic foot ulcers (DFUs) are a common and serious complication of diabetes, presenting as open sores or wounds on the sole. They result from impair...