Endocrinology

Implementation of A New, Mobile Diabetic Retinopathy Screening Model Incorporating Artificial Intelligence in Remote Western Australia.

OBJECTIVE: Diabetic retinopathy (DR) screening rates are poor in remote Western Australia where comm...

[Ten-year development and prospects of robotic thyroid surgery in China].

The robotic surgical system is a comprehensive system integrating multiple modern high technologies....

Identification of hypertension subtypes using microRNA profiles and machine learning.

OBJECTIVE: Hypertension is a major cardiovascular risk factor affecting about 1 in 3 adults. Althoug...

Data-Driven Strategies for Carbimazole Titration: Exploring Machine Learning Solutions in Hyperthyroidism Control.

BACKGROUND: University Hospitals Dorset (UHD) has more than 1000 thyroid patient contacts annually. ...

Mitochondrial mt12361A>G increased risk of metabolic dysfunction-associated steatotic liver disease among non-diabetes.

BACKGROUND: Insulin resistance, lipotoxicity, and mitochondrial dysfunction contribute to the pathog...

MUTATE: a human genetic atlas of multiorgan artificial intelligence endophenotypes using genome-wide association summary statistics.

Artificial intelligence (AI) has been increasingly integrated into imaging genetics to provide inter...

Using gut microbiome metagenomic hypervariable features for diabetes screening and typing through supervised machine learning.

Diabetes mellitus is a complex metabolic disorder and one of the fastest-growing global public healt...

Two-Stage Deep Learning Model for Adrenal Nodule Detection on CT Images: A Retrospective Study.

Background The detection and classification of adrenal nodules are crucial for their management. Pur...

System Dynamics Modeling for Diabetes Treatment and Prevention Planning.

The increasing prevalence of preventable chronic disease in Canada poses significant challenges to b...

Can metformin prevent cancer relative to sulfonylureas? A target trial emulation accounting for competing risks and poor overlap via double/debiased machine learning estimators.

There is mounting interest in the possibility that metformin, indicated for glycemic control in type...

Non-Linear Dose-Response Relationship for Metformin in Japanese Patients With Type 2 Diabetes: Analysis of Irregular Longitudinal Data by Interpretable Machine Learning Models.

The dose-response relationship between metformin and change in hemoglobin A1c (HbA1c) shows a maximu...

ASSESSMENT OF ARTIFICIAL INTELLIGENCE SOFTWARE FOR AUTOMATIC SCREENING OF DIABETIC RETINOPATHY BASED ON FUNDUS PHOTOGRAPHS IN MELANODERM SUBJECTS.

PURPOSE: To assess the Gaiha Prio Retino +™ Artificial Intelligence (AI) software for detecting diab...

Machine Learning May Be an Alternative to BIPSS in the Differential Diagnosis of ACTH-dependent Cushing Syndrome.

CONTEXT: Artificial intelligence research in the field of neuroendocrinology has accelerated. It is ...

Predicting Diabetic Retinopathy Using a Machine Learning Approach Informed by Whole-Exome Sequencing Studies.

OBJECTIVE: To establish and validate a novel diabetic retinopathy (DR) risk-prediction model using a...

[Advances in the application of artificial intelligence technology in the surgical diagnosis and treatment of thyroid cancer].

The multikinetic fusion feature of PPG was combined with MCNN_vision_transformer for diabetes detection.

BACKGROUND: Diabetes is a chronic condition that significantly impacts the cardiovascular system and...

A novel approach to finding the compositional differences and biomarkers in gut microbiota in type 2 diabetic patients via meta-analysis, data-mining, and multivariate analysis.

BACKGROUND/PURPOSE OF THE STUDY: Type 2 diabetes mellitus (T2DM)-one of the fastest globally spreadi...

Machine learning-based coronary heart disease diagnosis model for type 2 diabetes patients.

BACKGROUND: To establish a classification model for assisting the diagnosis of type 2 diabetes melli...

Intelligent diagnosis of thyroid nodules with AI ultrasound assistance and cytology classification.

OBJECTIVE: Accurate evaluation of thyroid nodules is crucial for effective management; however, meth...

Development of an artificial intelligence-based convolutional neural network for sellar barrier classification using magnetic resonance imaging.

BACKGROUND: This study aims to develop an artificial intelligence (AI) model using convolutional neu...

A nomogram for predicting the nature of thyroid adenomatoid nodules on ultrasound: a dual-center study.

PURPOSE: Thyroid Imaging Reporting and Data System (TIRADS) does not perform well in thyroid adenoma...