Latest AI and machine learning research in endocrinology for healthcare professionals.
BACKGROUND: Type 2 diabetes (T2D) currently has no cure. However, extensive evidence suggests that addressing key risk factors through lifestyle changes can help individuals effectively self-manage their condition. Diabetes self-management primarily involves patients engaging in self-monitoring behaviors and adopting coping strategies to manage their long-term illness. In recent years, health coac...
Depression is a prevalent and disabling syndrome characterized by sustained sadness and/or anhedonia, as well as cognitive and physical symptoms. In Parkinson's disease (PD), depression is both common and clinically challenging due to overlapping symptoms and complex etiologic interactions. Major depressive disorder occurs in approximately 17% of PD patients, while clinically significant depressiv...
CONTEXT: Overcoming the disconnect between efficient Machine Learning (ML) techniques and its therapeutic applicability necessitates frameworks that p...
Emerging evidence suggests that diabetes mellitus (DM) is not only a metabolic disorder but also a mucosal disease shaped by microbial interactions ac...
BACKGROUND: The integration of artificial intelligence into retinal practice represents more than a technological advancement; it constitutes an anthr...
BACKGROUND: Diabetes care requires frequent and high-stakes decisions that must be made in the setting of substantial day-to-day physiologic variabili...
Research on theoretical prediction methods for elucidating the reaction mechanisms and thyroid hormone-disrupting effects of emerging pollutants in th...
BackgroundDementia is a common complication of type 2 diabetes mellitus (T2DM), influenced by both genetic susceptibility and social disadvantages. Wh...
The synchronous monitoring of stress hormones and metabolic indicators is crucial for long-term health management, yet it remains a significant challe...
Deep learning (DL) has enabled automated segmentation of ultrasound images, and due to the rapid development of DL models, we want to offer a comprehe...
BACKGROUND: Automated identification of postprandial glucose responses (PPGR) from continuous glucose monitoring (CGM) profiles may detect early dysgl...
INTRODUCTION: Hormone deficiency states are increasingly recognized as important, yet often underappreciated, contributors to cardiovascular (CV) dise...
Vascular and lymphatic vessel regeneration is crucial for tissue repair and organ function restoration. However, conventional biomaterials are often c...
AIMS: Diabetes mellitus shortens life expectancy, driven primarily by premature mortality from vascular complications. Mortality models for intensive ...
Type 2 diabetes (T2D) and its complications represent a complex disorder involving multiple pathophysiological processes. Although conventional therap...
The de novo design of small-molecule-binding proteins holds great promise as a potential tool to develop sensors on-demand for arbitrary small molecul...
PURPOSE: To evaluate longitudinal change in hard exudate (HEs) volume over 5-years following anti-VEGF treatment for diabetic macular oedema (DMO). ME...
AIMS: To map and systematise existing research on the use of artificial intelligence (AI) in mental health-based diabetes care contexts, identify tren...
BACKGROUND: Insulin resistance is a central pathophysiological feature of cardiovascular-kidney-metabolic (CKM) syndrome and has been implicated in ad...
PURPOSE: Artificial intelligence (AI) has emerged as a pivotal tool in enhancing the management of gestational diabetes mellitus (GDM). With its risin...