Endocrinology

Machine learning in lymphocyte and immune biomarker analysis for childhood thyroid diseases in China.

OBJECTIVE: This study aims to characterize and analyze the expression of representative biomarkers like lymphocytes and immune subsets in children with thyroid disorders. It also intends to develop and evaluate a machine learning model to predict if patients have thyroid disorders based on their clinical characteristics, ultimately providing insights to enhance the clinical guidelines for the path...

Real-World Type 2 Diabetes Second-Line Treatment Allocation Among Patients.

OBJECTIVE: This study aimed to evaluate the impact of socioeconomic disparities on the allocation of second-line treatments among patients with type 2 diabetes (T2D).

Toward Accurate Deep Learning-Based Prediction of Ki67, ER, PR, and HER2 Status From H&E-Stained Breast Cancer Images.

Despite improvements in machine learning algorithms applied to digital pathology, only moderate accuracy, to predict molecular information from histol...

Identification of mitochondria-related feature genes for predicting type 2 diabetes mellitus using machine learning methods.

PURPOSE: We aimed to identify the mitochondria-related feature genes associated with type 2 diabetes mellitus and explore their potential roles in imm...

Future horizons in diabetes: integrating AI and personalized care.

Diabetes is a global health crisis with rising incidence, mortality, and economic burden. Traditional markers like HbA1c are insufficient for capturin...

Interpretable machine learning-based insights into early-life endocrine disruptor exposure and small vulnerable newborns.

Early-life exposure to endocrine-disrupting chemicals (EDCs) may contribute to small vulnerable newborns, including conditions such as being small for...

Interpretable machine learning method to predict the risk of pre-diabetes using a national-wide cross-sectional data: evidence from CHNS.

OBJECTIVE: The incidence of Type 2 Diabetes Mellitus (T2DM) continues to rise steadily, significantly impacting human health. Early prediction of pre-...

Thyroid nodule classification in ultrasound imaging using deep transfer learning.

BACKGROUND: The accurate diagnosis of thyroid nodules represents a critical and frequently encountered challenge in clinical practice, necessitating e...

Brain tumor intelligent diagnosis based on Auto-Encoder and U-Net feature extraction.

Preoperative classification of brain tumors is critical to developing personalized treatment plans, however existing classification methods rely on ma...

Which approach better predicts diabetes: Traditional econometric methods or machine learning? Evidence from a cross-sectional study in South Korea.

To prevent chronic disease from getting worse, it is important to detect and predict it at an early stage. Therefore, the accuracy of the prediction i...

Mitigation of the toxic effects of nitrite: Role and mechanism of isoleucine in mitigating mitochondrial DNA leakage-induced inflammation in grass carp (Ctenopharyngodon idella) under nitrite exposure.

The physiological and growth processes of fish are closely associated with their surrounding environment. This study investigated the role and underly...

Learning from the machine: is diabetes in adults predicted by lifestyle variables? A retrospective predictive modelling study of NHANES 2007-2018.

OBJECTIVES: This study aimed to compare the performance of five machine learning algorithms to predict diabetes mellitus based on lifestyle factors (d...

Personalized Blood Glucose Forecasting From Limited CGM Data Using Incrementally Retrained LSTM.

For people with Type 1 diabetes (T1D), accurate blood glucose (BG) forecasting is crucial for the effective delivery of insulin by Artificial Pancreas...

A diagnostic model for polycystic ovary syndrome based on machine learning.

Diagnosis of polycystic ovary syndrome remains a challenge. In this study, we propose constructing a diagnostic model of polycystic ovary syndrome by ...

The relationship between epigenetic biomarkers and the risk of diabetes and cancer: a machine learning modeling approach.

INTRODUCTION: Epigenetic biomarkers are molecular indicators of epigenetic changes, and some studies have suggested that these biomarkers have predict...

Finding Optimal Alphabet for Encoding Daily Continuous Glucose Monitoring Time Series Into Compressed Text.

BACKGROUND: The emergence of continuous glucose monitoring (CGM) devices has not only revolutionized diabetes management but has also opened new avenu...

Can Artificial Intelligence Software be Utilised for Thyroid Multi-Disciplinary Team Outcomes?

OBJECTIVES: ChatGPT is one of the most publicly available artificial intelligence (AI) softwares. Ear, nose and throat (ENT) services are often stretc...

Machine Learning-Assisted Portable Dual-Readout Biosensor for Visual Detection of Milk Allergen.

Beta-lactoglobulin (β-LG), the primary allergen in cow's milk, makes developing a rapid, sensitive, and convenient detection method essential for indi...

Machine learning applications to classify and monitor medication adherence in patients with type 2 diabetes in Ethiopia.

BACKGROUND: Medication adherence plays a crucial role in determining the health outcomes of patients, particularly those with chronic conditions like ...

Predicting the risks of stroke, cardiovascular disease, and peripheral vascular disease among people with type 2 diabetes with artificial intelligence models: A systematic review and meta-analysis.

Macrovascular complications, including stroke, cardiovascular disease (CVD), and peripheral vascular disease (PVD), significantly contribute to morbid...