Endocrinology

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

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A deep learning approach to understanding controlled ovarian stimulation and in vitro fertilization dynamics.

Infertility, recognized by the World Health Organization (WHO) as a disease affecting the male or fe...

D-GET: Group-Enhanced Transformer for Diabetic Retinopathy Severity Classification in Fundus Fluorescein Angiography.

Early detection of Diabetic Retinopathy (DR) is vital for preserving vision and preventing deteriora...

Comparison of the accuracy of GPT-4 and resident physicians in differentiating benign and malignant thyroid nodules.

OBJECTIVE: To assess the diagnostic performance of the GPT-4 model in comparison to resident physici...

An early prediction model for gestational diabetes mellitus created using machine learning algorithms.

OBJECTIVE: To investigate high-risk factors for gestational diabetes mellitus (GDM) in early pregnan...

Two-step pragmatic subgroup discovery for heterogeneous treatment effects analyses: perspectives toward enhanced interpretability.

Effect heterogeneity analyses using causal machine learning algorithms have gained popularity in rec...

Investigation and validation of genes associated with endoplasmic reticulum stress in diabetic retinopathy using various machine learning algorithms.

BACKGROUND: Diabetic retinopathy (DR) is a common complication of diabetes, with Endoplasmic reticul...

Performance of a Deep Learning Diabetic Retinopathy Algorithm in India.

IMPORTANCE: While prospective studies have investigated the accuracy of artificial intelligence (AI)...

Machine learning-based risk predictive models for diabetic kidney disease in type 2 diabetes mellitus patients: a systematic review and meta-analysis.

BACKGROUND: Machine learning (ML) models are being increasingly employed to predict the risk of deve...

Artificial intelligence based predictive tools for identifying type 2 diabetes patients at high risk of treatment Non-adherence: A systematic review.

AIMS: Several Artificial Intelligence (AI) based predictive tools have been developed to predict non...

Machine Learning-Driven D-Glucose Prediction Using a Novel Biosensor for Non-Invasive Diabetes Management.

Developing reliable noninvasive diagnostic and monitoring systems for diabetes remains a significant...

Spatial analysis of air pollutant exposure and its association with metabolic diseases using machine learning.

BACKGROUND: Metabolic diseases (MDs), exemplified by diabetes, hypertension, and dyslipidemia, have ...

GALR1 and PENK serve as potential biomarkers in invasive non-functional pituitary neuroendocrine tumours.

BACKGROUND: Some nonfunctioning pituitary neuroendocrine tumor (NFPitNET) can show invasive growth, ...

T1-weighted MRI-based brain tumor classification using hybrid deep learning models.

Health is fundamental to human well-being, with brain health particularly critical for cognitive fun...

AI/ML modeling to enhance the capability of in vitro and in vivo tests in predicting human carcinogenicity.

This study aimed to develop an in silico model for predicting human carcinogenicity using advanced d...

The Central Role of Learning in Preventing Foot Complications in Persons With Diabetes: A Scoping Review.

BACKGROUND: Despite a variety of literature reviews, there is limited understanding of the learning ...

A feature explainability-based deep learning technique for diabetic foot ulcer identification.

Diabetic foot ulcers (DFUs) are a common and serious complication of diabetes, presenting as open so...

Early gestational diabetes mellitus risk predictor using neural network with NearMiss.

BACKGROUND: Gestational diabetes mellitus (GDM) is globally recognized as a significant pregnancy-re...

MSTNet: Multi-scale spatial-aware transformer with multi-instance learning for diabetic retinopathy classification.

Diabetic retinopathy (DR), the leading cause of vision loss among diabetic adults worldwide, undersc...

Artificial Intelligence in CT for Predicting Cervical Lymph Node Metastasis in Papillary Thyroid Cancer Patients: A Meta-analysis.

PURPOSE: This meta-analysis aims to evaluate the diagnostic performance of CT-based artificial intel...

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