Endocrinology

Machine Learning Models for Predicting Type 2 Diabetes Complications in Malaysia.

This study aimed to develop machine learning (ML) models to predict diabetic complications in patients with type 2 diabetes (T2D) in Malaysia. Data from the Malaysian National Diabetes Registry and Death Register were used to develop predictive models for five complications: all-cause mortality, retinopathy, nephropathy, ischemic heart disease (IHD), and cerebrovascular disease (CeVD). Accurate pr...

Unsupervised machine learning analysis of optical coherence tomography radiomics features for predicting treatment outcomes in diabetic macular edema.

This study aimed to identify distinct clusters of diabetic macular edema (DME) patients with differential anti-vascular endothelial growth factor (VEGF) treatment outcomes using an unsupervised machine learning (ML) approach based on radiomic features extracted from pre-treatment optical coherence tomography (OCT) images. Retrospective data from 234 eyes with DME treated with three anti-VEGF thera...

Impact of Simplera Sync™ sensors and Extended™ Wear Infusion Sets on glycaemia and system performance of the MiniMed™ 780G system in children and young adults with previously high HbA1c.

AIMS: The MiniMed™ 780G improves glycaemia and reduces burden in type 1 diabetes. We investigated how new all-in-one "Simplera Sync™" sensors and 7-da...

Use of a priority lane to increase voluntary visits to a milking robot in dairy cows.

Voluntary visits to the milking robot are the basis of automatic milking system functionality. Therefore, problems arise when cows are undermotivated ...

Assessing the association of multi-environmental chemical exposures on metabolic syndrome: A machine learning approach.

Metabolic syndrome (MetS) is a major global public health concern due to its rising prevalence and association with increased risks of cardiovascular ...

Predicting cerebral infarction and transient ischemic attack in healthy individuals and those with dysmetabolism: a machine learning approach combined with routine blood tests.

Ischemic cerebral infarction is the most prevalent type of stroke, causing significant disability and death worldwide. Transient ischemic attack (TIA)...

Estimated glucose disposal rate outperforms other insulin resistance surrogates in predicting incident cardiovascular diseases in cardiovascular-kidney-metabolic syndrome stages 0-3 and the development of a machine learning prediction model: a nationwide prospective cohort study.

BACKGROUND: The American Heart Association recently introduced the concept of cardiovascular-kidney-metabolic (CKM) syndrome, highlighting the increas...

Diabetes: Non-Invasive Blood Glucose Monitoring Using Federated Learning with Biosensor Signals.

Diabetes is a growing global health concern, affecting millions and leading to severe complications if not properly managed. The primary challenge in ...

Machine learning for high-risk hospitalization prediction in outpatient individuals with diabetes at a tertiary hospital.

OBJECTIVE: To characterize, via a predictive model using real-world data, patients with diabetes with a heightened probability of hospitalization.

Detection of diabetic macular oedema patterns with fine-grained image categorisation on optical coherence tomography.

PURPOSE: To develop an artificial intelligence (AI) system for detecting pathological patterns of diabetic macular oedema (DME) with fine-grained imag...

A deep learning approach for blood glucose monitoring and hypoglycemia prediction in glycogen storage disease.

Glycogen storage disease (GSD) is a group of rare inherited metabolic disorders characterized by abnormal glycogen storage and breakdown. These disord...

Predicting diabetes mellitus metabolic goals and chronic complications transitions-analysis based on natural language processing and machine learning models.

OBJECTIVE: To estimate Diabetes mellitus (DM) progression at one and two years in terms of glycemic targets and development of complications.

Advanced computational tools, artificial intelligence and machine-learning approaches in gut microbiota and biomarker identification.

The microbiome of the gut is a complex ecosystem that contains a wide variety of microbial species and functional capabilities. The microbiome has a s...

Corrigendum to "Prediction of BRAF and TERT status in PTCs by machine learning-based ultrasound radiomics methods: A multicenter study" [J. Clin. Translat. Endocrinol. 40 (2025) 100390].

[This corrects the article DOI: 10.1016/j.jcte.2025.100390.].

Comparative study of XGBoost and logistic regression for predicting sarcopenia in postsurgical gastric cancer patients.

The use of machine learning (ML) techniques, particularly XGBoost and logistic regression, to predict sarcopenia among postsurgical gastric cancer pat...

Risk Factors and Outcomes of Hemorrhagic Transformation in Acute Ischemic Stroke Following Thrombolysis: Analysis of a Single-Center Experience and Review of the Literature.

: This is a retrospective study conducted at the Clinical County Hospital of Craiova, Romania, providing valuable insights into hemorrhagic transforma...

Predicting High Glycemia Risk Index Trajectory in Individuals With Type 1 Diabetes and Long-term Continuously Glucose Monitoring.

The glycemia risk index (GRI) is an emerging metric designed to quantify the risk of both hypo- and hyperglycemia, providing a combined assessment of ...

Artificial intelligence for early detection of diabetes mellitus complications via retinal imaging.

BACKGROUND: Diabetes mellitus (DM) increases the risk of vascular complications, and retinal vasculature imaging serves as a valuable indicator of bot...

Leveraging machine learning in precision medicine to unveil organochlorine pesticides as predictive biomarkers for thyroid dysfunction.

Exposure to organochlorine pesticides (OCPs) poses significant health risks, including cancer, endocrine dysregulation, neurological disorders, and re...

Predicting the efficacy of microwave ablation of benign thyroid nodules from ultrasound images using deep convolutional neural networks.

BACKGROUND: Thyroid nodules are frequent in clinical settings, and their diagnosis in adults is growing, with some persons experiencing symptoms. Ultr...