AI Medical Compendium Journal:
Frontiers in endocrinology

Showing 71 to 80 of 141 articles

The role of machine learning in advancing diabetic foot: a review.

Frontiers in endocrinology
BACKGROUND: Diabetic foot complications impose a significant strain on healthcare systems worldwide, acting as a principal cause of morbidity and mortality in individuals with diabetes mellitus. While traditional methods in diagnosing and treating th...

Advances in artificial intelligence in thyroid-associated ophthalmopathy.

Frontiers in endocrinology
Thyroid-associated ophthalmopathy (TAO), also referred to as Graves' ophthalmopathy, is a medical condition wherein ocular complications arise due to autoimmune thyroid illness. The diagnosis of TAO, reliant on imaging, typical ocular symptoms, and a...

An ensemble deep learning diagnostic system for determining Clinical Activity Scores in thyroid-associated ophthalmopathy: integrating multi-view multimodal images from anterior segment slit-lamp photographs and facial images.

Frontiers in endocrinology
BACKGROUND: Thyroid-associated ophthalmopathy (TAO) is the most prevalent autoimmune orbital condition, significantly impacting patients' appearance and quality of life. Early and accurate identification of active TAO along with timely treatment can ...

Exploring deep learning radiomics for classifying osteoporotic vertebral fractures in X-ray images.

Frontiers in endocrinology
PURPOSE: To develop and validate a deep learning radiomics (DLR) model that uses X-ray images to predict the classification of osteoporotic vertebral fractures (OVFs).

A Siamese ResNeXt network for predicting carotid intimal thickness of patients with T2DM from fundus images.

Frontiers in endocrinology
OBJECTIVE: To develop and validate an artificial intelligence diagnostic model based on fundus images for predicting Carotid Intima-Media Thickness (CIMT) in individuals with Type 2 Diabetes Mellitus (T2DM).

Predictive value of ultrasonic artificial intelligence in placental characteristics of early pregnancy for gestational diabetes mellitus.

Frontiers in endocrinology
BACKGROUND: To explore the predictive value of placental features in early pregnancy for gestational diabetes mellitus (GDM) using deep and radiomics-based machine learning (ML) applied to ultrasound imaging (USI), and to develop a nomogram in conjun...

Prediction of immunotherapy response in idiopathic membranous nephropathy using deep learning-pathological and clinical factors.

Frontiers in endocrinology
BACKGROUND: Owing to individual heterogeneity, patients with idiopathic membranous nephropathy (IMN) exhibit varying sensitivities to immunotherapy. This study aimed to establish and validate a model incorporating pathological and clinical features u...

Machine learning-based prediction of vitamin D deficiency: NHANES 2001-2018.

Frontiers in endocrinology
BACKGROUND: Vitamin D deficiency is strongly associated with the development of several diseases. In the current context of a global pandemic of vitamin D deficiency, it is critical to identify people at high risk of vitamin D deficiency. There are n...

Immunization against inhibin DNA vaccine as an alternative therapeutic for improving follicle development and reproductive performance in beef cattle.

Frontiers in endocrinology
The objective of the present study was to investigate the potential role of immunization against INH on follicular development, serum reproductive hormone (FSH, E, and P) concentrations, and reproductive performance in beef cattle. A total of 196 non...

deepPGSegNet: MRI-based pituitary gland segmentation using deep learning.

Frontiers in endocrinology
INTRODUCTION: In clinical research on pituitary disorders, pituitary gland (PG) segmentation plays a pivotal role, which impacts the diagnosis and treatment of conditions such as endocrine dysfunctions and visual impairments. Manual segmentation, whi...