Endocrinology

Machine Learning Models to Evaluate County-Level Incidence of Diagnosed Diabetes and Sociodemographic Factors.

AIMS: To evaluate county-level incidence of diagnosed diabetes and key sociodemographic factors in a high-dimensional, nonlinear setting. METHODS: This temporally aggregated observational study used US Centers for Disease Control and Prevention data on county-level incidence of diagnosed diabetes, from 2004 to 2019, and 34 sociodemographic factors from public databases. We defined counties as high...

Dosimetric comparison of intensity modulated proton therapy and knowledge-based volumetric arc therapy for left-sided breast cancer.

INTRODUCTION: There is increasing interest in the application of proton beam therapy to left-sided breast cancer, due itspotential to reduce heart dose. However, state-of-the-art volumetric modulated arc therapy (VMAT) in conjunction with deep-inspiration breath hold (DIBH) and knowledge-based (KB) planning has demonstrated that low heart doses can be achieved in left-sided breast cancer with X-ra...

Assessing the Performance and Reliability of Deep Learning Auto-Segmentation in Videofluoroscopic Swallowing Studies: A Systematic Review and Meta-Analysis.

OBJECTIVE: To systematically evaluate the accuracy and reliability of deep learning-based auto-segmentation methods in videofluoroscopic swallowing st...

Algorithm-guided insulin therapy in hospitalized patients: current evidence, implementation challenges, and future perspectives.

Clinically Confirmed Cohort Reveals Antioxidant Genetic Polymorphisms as Potential Susceptibility Factors for Long COVID After Mild or Asymptomatic COVID-19.

Although the COVID-19 pandemic has now been down-graded, long COVID (LC) presents an ongoing risk of long-term disease for a significant percentage of...

Artificial intelligence-assisted FTIR spectroscopy for hormone receptor subtyping in formalin-fixed breast Cancer tissues.

BACKGROUND: Determination of estrogen receptor (ER) and progesterone receptor (PR) status is critical for breast cancer subtyping and guiding endocrin...

Precision medicine in thyroid nodules: current strategies, challenges, and future directions.

Perspective on applicability of data-driven machine learning computational new approach methodologies for hazard identification in chemicals risk assessment.

Machine Learning (ML) and Artificial Intelligence (AI) approaches have potential to make better-informed decisions in chemical hazard identification w...

Multi-dimensional CT feature screening, construction, and validation of a clinical diagnostic model for thyroid eye disease.

OBJECTIVE: Thyroid eye disease (TED) is an autoimmune condition associated with thyroid dysfunction, often presenting with complex and variable orbita...

Development and validation of a highly accurate multigene gene expression biomarker to predict chemotherapy response in primary triple-negative breast cancer.

PURPOSE: Triple-negative breast cancer (TNBC) is an aggressive subtype lacking estrogen and progesterone receptors and HER2 amplification. Representin...

Cardiovascular outcomes and subclinical atherosclerosis in metabolic-associated steatotic liver disease: long-term follow-up from a specialized outpatient cohort.

BACKGROUND AND AIMS: Cardiovascular disease is the main cause of mortality in metabolic-associated steatotic liver disease (MASLD). This study evaluat...

Metagenomic profiling and predictive modeling of the gut microbiome reveal signatures of gestational disease.

The gut microbiome plays a vital role in maternal health and pregnancy outcomes, yet its impact on conditions like gestational hypertension (GH) and g...

Ten-Year Trends in Healthcare Use and Costs for People Living with Diabetes in France: Results from the Epicodiab Study Based on National Claims Data.

INTRODUCTION: Diabetes remains a global public health concern, with increased prevalence and a significant economic burden. Yet, most studies do not d...

Can artificial intelligence bridge the gaps for primary diabetes care in low-income and middle-income countries?

Artificial intelligence (AI) has the potential to improve primary diabetes care in low-income and middle-income countries (LMICs), where the rising bu...

Imaging-Based Prediction of Key Breast Cancer Biomarkers Using Deep Learning on Digital Breast Tomosynthesis.

OBJECTIVE: To evaluate the feasibility of using deep learning models applied to digital breast tomosynthesis (DBT) images for non-invasive prediction ...

Longitudinal and Multimodal Recording System to Capture Real-World Patient-Clinician Conversations for AI and Encounter Research: Protocol for an Observational Study.

BACKGROUND: The promise of artificial intelligence (AI) in medicine depends on its ability to learn from data that reflect what matters to patients an...

An enhanced diabetic retinopathy detection approach using optimized deep learning technique.

Diabetic Retinopathy (DR) remains a leading cause of vision loss among diabetic patients, underscoring the importance of early detection through relia...

Digital twin technologies for supporting self-care in adults with diet-related chronic conditions: a systematic review.

BACKGROUND: Diet-related chronic conditions are major contributors to global morbidity and mortality. Effective management of these conditions require...

Sex Differences in the Associations Between Visceral Adiposity and Hepatic Insulin Clearance in Type 2 Diabetes Mellitus: A Quantitative CT Study.

AIMS: To investigate sex-related differences in the association of body fat distribution with hepatic insulin clearance (HIC) in type 2 diabetes melli...

Development and validation of a risk prediction model for diabetic peripheral neuropathic pain in type 2 diabetes: A machine learning and statistical approach.

OBJECTIVE: To identify independent risk factors for diabetic peripheral neuropathic pain (DPNP), construct a nomogram prediction model, and quantify t...