Endocrinology

Artificial intelligence methods in gestational diabetes mellitus prediction: A systematic literature review.

Gestational diabetes mellitus (GDM) is the most common metabolic disorder in pregnancy, posing risks to both maternal and neonatal health. Artificial intelligence (AI) and machine learning (ML)-based solutions hold the promise of improving GDM prediction, thus enabling earlier and more personalized care. The main objective of this systematic review is to provide a comprehensive overview of AI/ML m...

Artificial intelligence in obstetrics and gynecology: Evaluating ChatGPT and Google Gemini in answering patient questions.

INTRODUCTION: To evaluate the accuracy and completeness of responses across common obstetrical and gynecologic topics generated by the large language models (LLMs) ChatGPT and Google Gemini, which have become increasingly popular for patients seeking medical information before physician consultations. METHODS: Ten topics were identified, five obstetrical (prenatal labs, extended carrier screen, tr...

Unraveling the shared genetic architecture of osteoarthritis and metabolic traits through multi-omics insights.

OBJECTIVE: Osteoarthritis (OA) often coexists with metabolic traits (MTs), causing significant disability. Our study aims to uncover the shared geneti...

Using precision medicine methods to identify disease stages for chronic limb-threatening ischemia in participants of the BEST-CLI trial.

BACKGROUND: Chronic limb-threatening ischemia (CLTI), the most severe form of peripheral artery disease, is associated with a high risk of limb loss. ...

In silico prediction of endocrine activity.

Endocrine-disrupting chemicals (EDCs) pose health risks; yet, conventional in vitro and in vivo testing remains slow, costly, and animal-intensive. En...

Automatic Follicle Counting From Ultrasound Images of Ovaries Using MARDSE-UNET Model.

Detecting ovarian structures in ultrasound images is essential in gynecological and reproductive medicine. An automated detection system can serve as ...

Association between triglyceride-glucose index and depression and the mediating role of hypertension: a cross-sectional study of obese adults.

BACKGROUND: Recent findings indicate a positive correlation between the TyG (triglyceride-glucose) index and the incidence of depression. However, the...

Use of indirect methods and machine learning algorithms for the estimation of reference intervals, taking cortisol measurements as an example.

OBJECTIVES: This study aims to determine reliable reference intervals (RIs) for total cortisol (TC) in adults considering the effects of both age and ...

A Five-Plasma Protein-Based Algorithm for Predicting Incident CKD in Type 2 Diabetes.

KEY POINTS: Proteomics analyses consistently identified nine independent protein predictors for CKD in both Chinese and European participants with typ...

Interpretable type 2 diabetes incidence prediction with AutoScore: A model based on standard clinical parameters.

OBJECTIVE: Accurate prediction of type 2 diabetes mellitus (T2DM) onset is critical to enable timely interventions and preventive strategies. Although...

Identification of GPX3 as a key biomarker of placental ferroptosis in gestational diabetes mellitus via bioinformatics and clinical analysis.

Gestational diabetes mellitus (GDM) is characterized by glucose intolerance during pregnancy, and emerging evidence implicates dysregulated iron metab...

From objective grouping to fuzzy reference intervals: A standardized machine learning approach for thyroid function tests.

BACKGROUND: Accurate interpretation of thyroid function tests (TFTs) requires reliable reference intervals (RIs). Indirect methods based on retrospect...

Precision Nutritional Genomics, Gut Microbiota and Artificial Intelligence in Chronic Kidney Disease.

Chronic kidney disease (CKD) is a prevalent global health issue, and nutritional management of CKD is an integral component through all stages of the ...

Detection of Lymph Node Metastasis in Thyroid Cancer Using Deep Learning and Second Harmonic Generation Imaging.

Papillary thyroid carcinoma (PTC) is the most prevalent type of thyroid cancer, with a significant proportion of patients being susceptible to lymph n...

Roadmap for Representative Artificial Intelligence Models for Thyroid Cancer.

Artificial intelligence (AI) models in thyroid cancer are often not generalizable due to biased or non-representative training and validation datasets...

Brain metabolic imaging with 18 F-PET-CT and machine-learning clustering analysis reveal divergent metabolic phenotypes in patients with amyotrophic lateral sclerosis.

BACKGROUND: Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disorder characterized by significant clinicopathologic heterogeneity. Th...

Carbohydrate counting in traditional Turkish fast foods for individuals with type 1 diabetes: Can artificial intelligence models replace dietitians?

OBJECTIVES: Carbohydrate counting is a recommended approach for achieving glycemic control in individuals with type 1 diabetes (T1D). This study aimed...

Dual-feature cross-fusion network for precise brain tumor classification: a neurocomputational approach.

Brain tumors represent a significant neurological challenge, affecting individuals across all age groups. Accurate and timely diagnosis of tumor types...

Developing and Evaluating a Natural Language Processing Algorithm to Detect Insulin Pump and Continuous Glucose Monitor Use in Electronic Health Records of Patients with Type 1 Diabetes.

OBJECTIVE: We aimed to develop and validate natural language processing (NLP) algorithms to identify insulin pump and continuous glucose monitor (CGM)...

Precise prevention of DEHP induced hepatic fibrosis: Early identifying high-risk populations, revealing key factors, and applying targeted intervention.

Metabolic dysfunction-associated fatty liver disease (MAFLD) poses a serious threat to human health. Hepatic fibrosis is a decisive factor in the deat...