Endocrinology

Data-driven diabetes mellitus prediction and management: a comparative evaluation of decision tree classifier and artificial neural network models along with statistical analysis.

Diabetes Mellitus is a chronic metabolic disorder affecting a substantial global population leading to complications such as retinopathy, nephropathy, neuropathy, foot problems, heart attacks, and strokes if left unchecked. Prompt detection and diagnosis are crucial in managing and averting these complications. This study compares the effectiveness of a Decision Tree Classifier and an Artificial N...

A Deep Learning-Based Artificial Intelligence Model Assisting Thyroid Nodule Diagnosis and Management: Pilot Results for Evaluating Thyroid Malignancy in Pediatric Cohorts.

Artificial intelligence (AI) models have shown promise in predicting malignant thyroid nodules in adults; however, research on deep learning (DL) for pediatric cases is limited. We evaluated the applicability of a DL-based model for assessing thyroid nodules in children. We retrospectively identified two pediatric cohorts ( = 128; mean age 15.5 ± 2.4 years; 103 girls) who had thyroid nodule ultr...

Application research of artificial intelligence software in the analysis of thyroid nodule ultrasound image characteristics.

Thyroid nodule, as a common clinical endocrine disease, has become increasingly prevalent worldwide. Ultrasound, as the premier method of thyroid imag...

The Application of Machine Learning in Warfarin Dose Precision for Diabetic Patients Treated with Statins: A Comparative Study.

PURPOSE: To evaluate the impact of statin therapy on warfarin dose requirements in diabetic patients and to assess the performance of various machine ...

From Guidelines to Intelligence: How AI Refines Thyroid Nodule Biopsy Decisions.

OBJECTIVE: To evaluate the value of combining American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS) with the Demetic...

Assessing the stress hyperglycemia ratio in hyperlipidemia patients to predict all-cause mortality: A retrospective cohort study.

BACKGROUND: The Stress Hyperglycemia Ratio (SHR) reflects stress-related hyperglycemia and is linked to poor outcomes in various diseases. This study ...

Revolutionizing diabetes care: the role of artificial intelligence in prevention, diagnosis, and patient care.

UNLABELLED: Millions of people worldwide have diabetes, a disease that is becoming more common and has substantial socioeconomic costs. Artificial int...

Multi-Omics-Based Characterization of DNA Methylation Episignatures for Papillary Thyroid Cancer in a Chinese Population.

Thyroid cancer is the most common endocrine malignancy, with papillary thyroid cancer (PTC) accounting for ∼80% of all cases. DNA methylation alterat...

Application of machine learning algorithms to identify risk factors for depression in type 2 diabetes mellitus patients: A Taiwan diabetes registry study.

BACKGROUND: We analyzed variables reported during routine clinical practice using a registrational database to estimate risk factors for depression in...

Evaluating the impact of metabolic indicators and scores on cardiovascular events using machine learning.

Cardiovascular diseases such as coronary artery disease, myocardial infarction, and heart failure impact millions of people annually globally and are ...

Artificial Intelligence improves follow-up appointment uptake for diabetic retinal assessment: a systematic review and meta-analysis.

BACKGROUND/OBJECTIVES: Artificial intelligence (AI) assessment of diabetic retinopathy (DR) instead of scarce trained specialists could potentially in...

Emerging technologies for assessing the occurrence, fate, effects, and remediation of plastics in the environment.

Plastic pollution and contamination originates from raw material handling, polymerization, compounding, and fabrication, contributing to environmental...

Integration of proteomics and artificial intelligence-driven OCT biomarker analysis in central retinal vein occlusion.

Retinal OCT biomarker analysis by artificial intelligence (AI) has not previously been integrated with proteomics. Here, we combined the two technique...

DPP-IV inhibitory peptides from highland barley via machine learning and multi-scale validation.

Highland barley has shown potential in regulating blood glucose and may serve as a natural source of dipeptidyl peptidase-IV (DPP-IV) inhibitors. In t...

Combining structural equation modeling analysis with machine learning for early malignancy detection in Bethesda Category III thyroid nodules.

Atypia of Undetermined Significance (AUS), classified as Category III in the Bethesda Thyroid Cytopathology Reporting System, presents significant dia...

Artificial Intelligence's Role in Improving Adverse Pregnancy Outcomes: A Scoping Review and Consideration of Ethical Issues.

: Adverse pregnancy outcomes (APOs), which include hypertensive disorders of pregnancy (gestational hypertension, preeclampsia, and related disorders)...

A machine learning model for predicting anatomical response to Anti-VEGF therapy in diabetic macular edema.

PURPOSE: To develop a machine learning model to predict anatomical response to anti-VEGF therapy in patients with diabetic macular edema (DME).

Integrative transcriptomics and single-cell transcriptomics analyses reveal potential biomarkers and mechanisms of action in papillary thyroid carcinoma.

OBJECTIVE: Papillary thyroid carcinoma (PTC) has a high recurrence rate and lacks reliable diagnostic biomarkers. This study aims to identify robust t...

A machine learning-based prediction of diabetic retinopathy using the Korea national health and nutrition examination survey (2008-2012, 2017-2021).

BACKGROUND: Machine learning technology that uses available clinical data to predict diabetic retinopathy (DR) can be highly valuable in medical setti...

Beyond the Trend Arrow: Potential Value of Artificial Intelligence-Supported Glucose Predictions for People with Type 1 Diabetes Using Continuous Glucose Monitoring Systems.

Advances in diabetes technologies such as continuous glucose monitoring (CGM) have provided significant opportunities to improve glycemic and quality-...