Endocrinology

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

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Interpretable AI-driven multi-objective risk prediction in heart failure patients with thyroid dysfunction.

INTRODUCTION: Heart Failure (HF) complicated by thyroid dysfunction presents a complex clinical chal...

Intimate partner violence and stress-related disorders: from epigenomics to resilience.

Intimate Partner Violence (IPV) is a major public health problem to be addressed with innovative and...

Predicting central lymph node metastasis in papillary thyroid microcarcinoma: a breakthrough with interpretable machine learning.

OBJECTIVE: To develop and validate an interpretable machine learning (ML) model for the preoperative...

Unveiling the molecular mechanisms of stigmasterol on diabetic retinopathy: BNM framework construction and experimental validation.

BACKGROUND: Diabetic retinopathy (DR), one of the most common complications of diabetes, severely im...

The diagnostic value of convolutional neural networks in thyroid cancer detection using ultrasound images.

OBJECTIVE: To extract and analyze the image features of two-dimensional ultrasound images and elasti...

Machine learning with decision curve analysis evaluates nutritional metabolic biomarkers for cardiovascular-kidney-metabolic risk: an NHANES analysis.

BACKGROUND: The American Heart Association recently introduced the concept of Cardiovascular-Kidney-...

SMART (artificial intelligence enabled) DROP (diabetic retinopathy outcomes and pathways): Study protocol for diabetic retinopathy management.

INTRODUCTION: Delayed diagnosis of diabetic retinopathy (DR) remains a significant challenge, often ...

The Hypothalamic Medial Preoptic Area-Paraventricular Nucleus Circuit Modulates Depressive-Like Behaviors in a Mouse Model of Postpartum Depression.

Estrogen fluctuations have been implicated in various mood disorders, including perimenopausal and p...

Sex-Specific Ensemble Models for Type 2 Diabetes Classification in the Mexican Population.

BACKGROUND: Type 2 diabetes (T2D) is considered a global pandemic by the World Health Organization (...

Brain tumor classification using MRI images and deep learning techniques.

Brain tumors pose a significant medical challenge, necessitating early detection and precise classif...

Using machine learning to predict patients with polycystic ovary disease in Chinese women.

OBJECTIVE: With an estimated global frequency ranging from5 % to 21 %, polycystic ovary syndrome (PC...

Measuring Cell Dimensions in Fission Yeast Using Machine Learning.

In fission yeast (Schizosaccharomyces pombe), cell length is a crucial indicator of cell cycle progr...

SwinDFU-Net: Deep learning transformer network for infection identification in diabetic foot ulcer.

BACKGROUND: The identification of infection in diabetic foot ulcers (DFUs) is challenging due to var...

Continuous glucose monitoring using machine learning models and IoT device data: A meta-analysis.

BACKGROUND: Machine learning offers diverse options for effectively managing blood glucose levels in...

Machine Learning and Augmented Intelligence Enables Prognosis of Type 2 Diabetes Prior to Clinical Manifestation.

BACKGROUND: The global incidence of type 2 diabetes (T2D) persists at epidemic proportions. Early di...

Using Machine Learning and Artificial Intelligence to Predict Diabetes Mellitus among Women Population.

BACKGROUND: Diabetes Mellitus is a chronic health condition (long-lasting) due to inadequate control...

Risk prediction of integrated traditional Chinese and western medicine for diabetes retinopathy based on optimized gradient boosting classifier model.

In order to take full advantage of traditional Chinese medicine (TCM) and western medicine, combined...

The role of the microbiota-gut-brain axis and artificial intelligence in cognitive health of pediatric obstructive sleep apnea: A narrative review.

Pediatric obstructive sleep apnea (OSA) is a prevalent sleep-related breathing disorder associated w...

Determinants of developing cardiovascular disease risk with emphasis on type-2 diabetes and predictive modeling utilizing machine learning algorithms.

This research aims to enhance our comprehensive understanding of the influence of type-2 diabetes on...

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