Endocrinology

Comparative analysis of artificial intelligence tools in generating diabetic retinopathy guidelines.

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. There...

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 a...

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 ...

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-...

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 an...

Gut microbiome research: Revealing the pathological mechanisms and treatment strategies of type 2 diabetes.

The high prevalence and disability rate of type 2 diabetes (T2D) caused a huge social burden to the ...

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 abn...

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 di...

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 wit...

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 ...

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 a...

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.].

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, provi...

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 predic...

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

BACKGROUND: Diabetes mellitus (DM) increases the risk of vascular complications, and retinal vascula...

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 grow...

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, endoc...

Optimizing Diabetic Retinopathy Screening at Primary Health Centres in India: A Cost-Effectiveness Analysis.

BACKGROUND: The eye care package under the Ayushman Bharat comprehensive primary healthcare programm...

Web-Based Explainable Machine Learning-Based Drug Surveillance for Predicting Sunitinib- and Sorafenib-Associated Thyroid Dysfunction: Model Development and Validation Study.

BACKGROUND: Unlike one-snap data collection methods that only identify high-risk patients, machine l...

Construction and validation of a deep learning-based diagnostic model for segmentation and classification of diabetic foot.

OBJECTIVE: This study aims to conduct an in-depth analysis of diabetic foot ulcer (DFU) images using...