Endocrinology

Effectiveness and safety of AI-driven closed-loop systems in diabetes management: a systematic review and meta-analysis.

BACKGROUND: Diabetes is a metabolic disease that can lead to severe cardiovascular diseases and neuropathy. The associated medical costs and complications make timely and effective management particularly important. Traditional diagnostic and management methods, like frequent glucose sampling and insulin injections, impose physical injuries on subjects. The development of artificial intelligence (...

Ensemble-based Convolutional Neural Networks for brain tumor classification in MRI: Enhancing accuracy and interpretability using explainable AI.

BACKGROUND: Accurate and efficient classification of brain tumors, including gliomas, meningiomas, and pituitary adenomas, is critical for early diagnosis and treatment planning. Magnetic resonance imaging (MRI) is a key diagnostic tool, and deep learning models have shown promise in automating tumor classification. However, challenges remain in achieving high accuracy while maintaining interpreta...

Interpretable machine learning insights into the association between PFAS exposure and diabetes mellitus.

BACKGROUND: Diabetes Mellitus (DM) is a global health concern with rising prevalence, and its link to PFAS exposure remains unclear. No machine learni...

Intervention through an intelligent technological platform for socio-emotional development and health promotion in adolescents with Type 1 Diabetes Mellitus (emoTICare): A study protocol for randomized controlled trial.

BACKGROUND: During adolescence, individuals face various challenges that require adequate psychosocial and emotional adjustment. Additionally, the pre...

A multidimensional prediction model for overtraining risk in youth soccer players: Integrating physiological and psychological markers.

Overtraining syndrome (OTS) poses a critical challenge in youth soccer, particularly during periods of rapid physiological maturation combined with hi...

Hub biomarkers and their clinical relevance in glycometabolic disorders: A comprehensive bioinformatics and machine learning approach.

BACKGROUND: Gluconeogenesis is a critical metabolic pathway for maintaining glucose homeostasis, and its dysregulation can lead to glycometabolic diso...

Prediction of Sepsis after Endourologic Kidney Stone Surgery: A Machine Learning Approach.

Sepsis secondary to urinary tract infection after kidney stone surgery is associated with considerable morbidity. Limited research examines the use o...

Droplet microfluidics integrated with machine learning reveals how adipose-derived stem cells modulate endocrine response and tumor heterogeneity in ER breast cancer.

Approximately 70% of breast cancer (BC) diagnoses are estrogen receptor positive (ER) with ∼40% of ER BC patients presenting resistance to endocrine ...

Understanding stress-induced illegitimate aggression: the role of physiological and psychological factors in police cadets.

To better understand the consequences of stress in realistic scenarios, police cadets were tasked with performing a police intervention under differin...

Deep Learning-Based Adrenal Gland Volumetry for the Prediction of Diabetes.

BACKGROUND: The long-term association between adrenal gland volume (AGV) and type 2 diabetes (T2D) remains unclear. We aimed to determine the associat...

Developing a Two-Dimensional Size-Exclusion Liquid Chromatography Platform for Isolating the High-Quality Extracellular Vesicles to Predict and Evaluate Metabolic Improvement Outcomes of Sleeve Gastrectomy.

The direct analysis of plasma exosomal components still presents significant challenges due to the interference of high concentrations of albumin, imm...

Machine learning-based stratification of prediabetes and type 2 diabetes progression.

BACKGROUND: Diabetes mellitus, a global health concern with severe complications, demands early detection and precise staging for effective management...

A Novel Three-Stage AI-Assisted Approach for Accurate Differential Diagnosis and Classification of NIFTP and Thyroid Neoplasms.

The recent introduction of the term non-invasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP) marked a pivotal shift in th...

Comprehensive framework for thyroid disorder diagnosis: Integrating advanced feature selection, genetic algorithms, and machine learning for enhanced accuracy and other performance matrices.

Thyroid hormones control crucial physiological activities, such as metabolism, oxidative stress, erythropoiesis, thermoregulation, and organ developme...

Interpretable deep fuzzy network-aided detection of central lymph node metastasis status in papillary thyroid carcinoma.

PURPOSE: The non-invasive assessment of central lymph node metastasis (CLNM) in patients with papillary thyroid carcinoma (PTC) plays a crucial role i...

When the Slide Knows the Sequence: Artificial Intelligence-Assisted Pathology Predicts Mutations in Thyroid Cancer.

Can we identify individuals at risk to develop multiple myeloma? A machine learning-based predictive model.

Multiple myeloma evolves unnoticed over years, and when diagnosed, organ damage is common. Electronic health records (EHR) can help in developing pred...

The Future of Automated Insulin Delivery Systems.

Automated insulin delivery (AID) systems have revolutionized diabetes care by integrating continuous glucose monitoring (CGM), insulin pumps, and adva...

The utility of an artificial intelligence model based on decision tree and evolution algorithm to evaluate steatotic liver disease in a primary care setting.

Many ways of classifying steatotic liver disease (SLD) with metabolic conditions have been proposed. Thus, SLD-related variables were verified using a...

High-Sensitivity Detection of C-Peptide Biomarker for Diabetes by Solid-State Nanopore Using Machine Learning Identification.

Accurate and early detection of C-peptide, a stable biomarker indicative of diabetes, is crucial for disease diagnosis, treatment, and prevention. Thi...