Endocrinology

First trimester prediction of gestational diabetes mellitus by machine learning in twin pregnancies.

INTRODUCTION: We aimed to develop a machine learning model for first-trimester prediction of gestational diabetes mellitus (GDM) in twin pregnancies using a prospective international, multi-center cohort and identify useful predictive markers. METHODS: Pregnant women with two live fetuses were enrolled at 11 + 0 to 13 + 6 weeks' gestation and followed until delivery. GDM was diagnosed at 24-28 wee...

AI-Assisted detection of corneal nerve structural abnormalities in early diabetic keratopathy: development and validation of a deep learning framework.

To develop a deep learning-based computer-aided diagnostic model for the automated identification of corneal microneuromas from in vivo confocal microscopy (IVCM) images and to preliminarily assess its potential clinical utility in the context of diabetic corneal neuropathy. This retrospective diagnostic accuracy study was conducted at the Ophthalmology Center of Renmin Hospital of Wuhan Universit...

Customized CNN Architectures Outperform Pre-Trained Models in Differentiating Normal Brain Tissues, Glioma, Meningioma, and Pituitary Tumors.

Early and accurate detection of brain tumors is essential for improving treatment outcomes and patient survival. While pre-trained deep learning model...

Head-to-head comparison of the ability of the cardiometabolic index and triglyceride-glucose index to predict 3-year major adverse cardiovascular events in patients with atrial fibrillation: insights from a community cohort.

BACKGROUND: Atrial fibrillation (AF) represents the most common sustained cardiac arrhythmia and confers an elevated risk of major adverse cardiovascu...

Leveraging machine learning to predict de novo skin malignancy following lung transplantation.

AIMS: This study aimed to predict post-transplant malignancy risks at multiple levels among lung transplant recipients using machine learning (ML) and...

Systemic Microvasculature Frailty: Brain frailty score predicts contrast-associated acute kidney injury after thrombectomy for stroke.

BACKGROUND: Contrast-associated acute kidney injury (CA-AKI) is a frequent complication after mechanical thrombectomy (MT). Cerebral small vessel dise...

Artificial Intelligence-assisted Risk Stratification of Thyroid Nodules with Atypia of Undetermined Significance.

OBJECTIVE: Given the limitations of conventional approaches in managing indeterminate thyroid nodules, there remains an unmet need for non-invasive as...

Vascular cognitive impairment and dementia: Prevention, treatments, mechanisms and management options for the future.

Vascular Cognitive Impairment and Dementia (VCID), the second most common form of dementia, is becoming increasingly prevalent worldwide. However, cur...

Fully closed-loop systems: can people with type 1 diabetes just do it? Insights from open-source systems.

Automated insulin delivery (AID) systems have significantly advanced diabetes management, progressively reducing user interactions required for optima...

Unveiling the relationship between stress-hyperglycemia ratio and cardiometabolic multimorbidity risk using interpretable machine learning.

BACKGROUND: Cardiometabolic multimorbidity (CMM) is the simultaneous manifestation of multiple cardiovascular and metabolic diseases, and it has arise...

Semi-Supervised Fatty Liver Classification Using Attention-Based Graph Neural Network Models.

BACKGROUND: Fatty liver disease is a common condition linked to metabolic syndrome, cardiovascular diseases, and liver cirrhosis, and timely, accurate...

Deciphering the molecular landscape of Sjögren's disease, mucosa-associated lymphoid tissue lymphoma, and thyroid cancer: unraveling the complexities of disease mechanisms and diagnostic biomarkers.

BACKGROUND: Sjögren's disease (SjD), mucosa-associated lymphoid tissue lymphoma (MALT lymphoma), and thyroid cancer (THCA) are clinically distinct yet...

Continuous Glucose Monitoring-Based Machine Learning Identification of Diurnal Glycemic Patterns and Diabetes Distress in Type 2 Diabetes.

BACKGROUND: To identify diurnal glycemic patterns in adults with type 2 diabetes (T2D) using continuous glucose monitoring (CGM)-based machine learnin...

Efficacy of an AI-Enabled Low Glucose Prediction: A Pooled Performance Analysis With Capillary Blood Glucose as Ground Truth.

BACKGROUND: Hypoglycemia is a critical challenge for insulin-dependent people with diabetes using multiple daily injections (MDI), who rely on reactiv...

A comprehensive biomechanical phenotyping framework for diabetic foot ulcer risk stratification using multi-modal gait analysis and machine learning.

BACKGROUND: Current diabetic foot ulcer risk assessment methods lack precision in identifying high-risk biomechanical phenotypes. This study aimed to ...

Machine learning and SHAP values for predicting coronary artery disease risk in Xinjiang, China.

BACKGROUND: Accurate individual risk assessment is crucial for guiding and improving the prevention of atherosclerotic cardiovascular disease (ASCVD)....

Activating the SDF-1/CXCR4 axis: Notoginsenoside R1-Functionalized zinc scaffolds accelerate fracture healing and angiogenesis in diabetic osteoporosis.

Effective treatment of diabetic osteoporotic fractures (DOF) requires biomaterials capable of promoting vascularized bone regeneration. A biodegradabl...

Machine Learning-Based Prediction of Distant Recurrence Risk and Ribociclib Treatment Effect in HR+/HER2- Early Breast Cancer Using Real-World and NATALEE Data.

PURPOSE: Despite current standard-of-care endocrine therapy, distant recurrence remains a concern for patients with hormone receptor-positive (HR+)/HE...

Identification and validation of PANX1 as an inflammasome-related biomarker in gestational diabetes mellitus: insights from machine learning and experimental approaches.

Gestational diabetes mellitus (GDM) is characterized by glucose intolerance during pregnancy, resulting from insulin resistance, and is associated wit...

Artificial intelligence-based fluid quantification predicts clinical outcomes in diabetic macular oedema eyes treated with intravitreal dexamethasone implants: DIADEMA project.

AIM: To explore associations between artificial intelligence (AI)-based baseline optical coherence tomography (OCT) fluid compartment quantifications ...