Endocrinology

Circulating miRNAs and Machine Learning for Lateralizing Primary Aldosteronism.

BACKGROUND: Distinguishing between unilateral and bilateral primary aldosteronism, a major cause of secondary hypertension, is crucial due to different treatment approaches. While adrenal venous sampling is the gold standard, its invasiveness, limited availability, and often difficult interpretation pose challenges. This study explores the utility of circulating microRNAs (miRNAs) and machine lear...

Assessment of the Diagnostic Performance of a Commercially Available Artificial Intelligence Algorithm for Risk Stratification of Thyroid Nodules on Ultrasound.

Thyroid nodules are challenging to accurately characterize on ultrasound (US), though the emergence of risk stratification systems and more recently artificial intelligence (AI) algorithms has improved nodule classification. The purpose of this study was to evaluate the performance of a recent Food and Drug Administration (FDA)-cleared AI tool for detection of malignancy in thyroid nodules on US....

Avoidable biopsies? Validating artificial intelligence-based decision support software in indeterminate thyroid nodules.

BACKGROUND: Multiple artificial intelligence (AI) systems have been approved to risk-stratify thyroid nodules through sonographic characterization. We...

Identification of key biomarkers for predicting atherosclerosis progression in polycystic ovary syndrome via bioinformatics analysis and machine learning.

OBJECTIVE: Polycystic ovary syndrome (PCOS) is one of the most significant cardiovascular risk factors, playing vital roles in various cardiovascular ...

Identification of endocrine-disrupting chemicals targeting key OP-associated genes via bioinformatics and machine learning.

Osteoporosis (OP), a metabolic disorder predominantly impacting postmenopausal women, has seen considerable progress in diagnosis and treatment over t...

Explainable Machine-Learning Models to Predict Weekly Risk of Hyperglycemia, Hypoglycemia, and Glycemic Variability in Patients With Type 1 Diabetes Based on Continuous Glucose Monitoring.

BACKGROUND AND OBJECTIVE: The aim of this study was to develop and validate explainable prediction models based on continuous glucose monitoring (CGM)...

Improving the diagnostic strategy for thyroid nodules: a combination of artificial intelligence-based computer-aided diagnosis system and shear wave elastography.

PURPOSE: Thyroid nodules are highly prevalent in the general population, posing a clinical challenge in accurately distinguishing between benign and m...

A Study of Serum Ferritin and Thyroid-Stimulating Hormone Levels in Male Patients with Androgenetic Alopecia.

BACKGROUND: Androgenetic alopecia (AGA) is a multifactorial disorder expressed by several genes and various environmental factors apart from nutrition...

Integrated approach of machine learning, Mendelian randomization and experimental validation for biomarker discovery in diabetic nephropathy.

AIM: To identify potential biomarkers and explore the mechanisms underlying diabetic nephropathy (DN) by integrating machine learning, Mendelian rando...

Machine learning and statistical models to predict all-cause mortality in type 2 diabetes: Results from the UK Biobank study.

AIMS: This study aims to compare the performance of contemporary machine learning models with statistical models in predicting all-cause mortality in ...

Enhancing dietary analysis: Using machine learning for food caloric and health risk assessment.

In the wake of growing concerns regarding diet-related health issues, this study investigates the application of machine learning methods to estimate ...

Employing Machine Learning Models to Predict Potential α-Glucosidase Inhibitory Plant Secondary Metabolites Targeting Type-2 Diabetes and Their Validation.

The need for new antidiabetic drugs is evident, considering the ongoing global burden of type-2 diabetes mellitus despite notable progress in drug dis...

Transition from sulfur autotrophic to mixotrophic denitrification: Performance with different carbon sources, microbial community and artificial neural network modeling.

To address the limitations inherent in both sulfur autotrophic denitrification (SAD) and heterotrophic denitrification (HD) processes, this study intr...

Enhancing severe hypoglycemia prediction in type 2 diabetes mellitus through multi-view co-training machine learning model for imbalanced dataset.

Patients with type 2 diabetes mellitus (T2DM) who have severe hypoglycemia (SH) poses a considerable risk of long-term death, especially among the eld...

Diabetes prediction model based on GA-XGBoost and stacking ensemble algorithm.

Diabetes, as an incurable lifelong chronic disease, has profound and far-reaching effects on patients. Given this, early intervention is particularly ...

Artificial intelligence in reproductive endocrinology: an in-depth longitudinal analysis of ChatGPTv4's month-by-month interpretation and adherence to clinical guidelines for diminished ovarian reserve.

OBJECTIVE: To quantitatively assess the performance of ChatGPTv4, an Artificial Intelligence Language Model, in adhering to clinical guidelines for Di...

Artificial intelligence-based pathological application to predict regional lymph node metastasis in Papillary Thyroid Cancer.

In this study, a model for predicting lymph node metastasis in papillary thyroid cancer was trained using pathology images from the TCGA(The Cancer Ge...

Predictive modeling of arginine vasopressin deficiency after transsphenoidal pituitary adenoma resection by using multiple machine learning algorithms.

This study aimed to predict arginine vasopressin deficiency (AVP-D) following transsphenoidal pituitary adenoma surgery using machine learning algorit...

Explainable machine learning model for predicting paratracheal lymph node metastasis in cN0 papillary thyroid cancer.

Prophylactic dissection of paratracheal lymph nodes in clinically lymph node-negative (cN0) papillary thyroid carcinoma (PTC) remains controversial. T...

Predicting diabetes in adults: identifying important features in unbalanced data over a 5-year cohort study using machine learning algorithm.

BACKGROUND: Imbalanced datasets pose significant challenges in predictive modeling, leading to biased outcomes and reduced model reliability. This stu...