Endocrinology

Latest AI and machine learning research in endocrinology for healthcare professionals.

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Deciphering the environmental chemical basis of muscle quality decline by interpretable machine learning models.

BACKGROUND: Sarcopenia is known as a decline in skeletal muscle quality and function that is associa...

A Scalable Application of Artificial Intelligence-Driven Insulin Titration Program to Transform Type 2 Diabetes Management.

Despite new pharmacotherapy, most patients with long-term type 2 diabetes are still hyperglycemic. ...

Establishment of a risk prediction model for olfactory disorders in patients with transnasal pituitary tumors by machine learning.

To construct a prediction model of olfactory dysfunction after transnasal sellar pituitary tumor res...

Analysis and interpretability of machine learning models to classify thyroid disease.

Thyroid disease classification plays a crucial role in early diagnosis and effective treatment of th...

An ensemble-based machine learning model for predicting type 2 diabetes and its effect on bone health.

BACKGROUND: Diabetes is a chronic condition that can result in many long-term physiological, metabol...

Artificial intelligence in retinal screening using OCT images: A review of the last decade (2013-2023).

BACKGROUND AND OBJECTIVES: Optical coherence tomography (OCT) has ushered in a transformative era in...

Identification of key genes and biological pathways associated with vascular aging in diabetes based on bioinformatics and machine learning.

Vascular aging exacerbates diabetes-associated vascular damage, a major cause of microvascular and m...

Evaluation of Artificial Intelligence Algorithms for Diabetic Retinopathy Detection: Protocol for a Systematic Review and Meta-Analysis.

BACKGROUND: Diabetic retinopathy (DR) is one of the most common complications of diabetes mellitus. ...

Pituitary MRI Radiomics Improves Diagnostic Performance of Growth Hormone Deficiency in Children Short Stature: A Multicenter Radiomics Study.

RATIONALE AND OBJECTIVES: To develop an efficient machine-learning model using pituitary MRI radiomi...

Comprehensive machine learning models for predicting therapeutic targets in type 2 diabetes utilizing molecular and biochemical features in rats.

INTRODUCTION: With the increasing prevalence of type 2 diabetes mellitus (T2DM), there is an urgent ...

DDLA: a double deep latent autoencoder for diabetic retinopathy diagnose based on continuous glucose sensors.

The current diagnosis of diabetic retinopathy is based on fundus images and clinical experience. How...

Hepatic toxicity prediction of bisphenol analogs by machine learning strategy.

Toxicological studies have demonstrated the hepatic toxicity of several bisphenol analogs (BPs), a p...

Smartphone based wearable sweat glucose sensing device correlated with machine learning for real-time diabetes screening.

BACKGROUND: Diabetes is a significant health threat, with its prevalence and burden increasing world...

Smart diabetic foot ulcer scoring system.

Current assessment methods for diabetic foot ulcers (DFUs) lack objectivity and consistency, posing ...

Feasibility and acceptance of artificial intelligence-based diabetic retinopathy screening in Rwanda.

BACKGROUND: Evidence on the practical application of artificial intelligence (AI)-based diabetic ret...

A novel fusion of genetic grey wolf optimization and kernel extreme learning machines for precise diabetic eye disease classification.

In response to the growing number of diabetes cases worldwide, Our study addresses the escalating is...

AI-enhanced integration of genetic and medical imaging data for risk assessment of Type 2 diabetes.

Type 2 diabetes (T2D) presents a formidable global health challenge, highlighted by its escalating p...

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