Endocrinology

Impact on Cost and Expert Time of Data-Efficient Deep Learning for Medical Image Segmentation.

Purpose To develop and systematically evaluate an iterative training approach, termed the expert-guided annotation loop, for efficient reference standard medical image segmentation, including assessment of two sample-selection strategies and real-world clinical implementation. Materials and Methods This retrospective study included ten datasets comprising 1948 CT or MRI examinations from autosomal...

Inflammation-mediated diabetes-liver disease cross-organ crosstalk: From molecular mechanisms to precision therapeutic strategies.

The rising global prevalence of diabetes mellitus and its liver complications presents a significant public health challenge. Inflammation-driven cross-organ communication plays a central role in linking these conditions. This review explores current insights into the molecular interactions between diabetes and liver disease, focusing on the role of inflammatory mediators in disease progression an...

Elucidating the therapeutic targets and multi-target mechanisms of salvianolic acid A for diabetic nephropathy.

Diabetic nephropathy (DN) is a leading cause of chronic kidney disease. Salvianolic acid A (SAA) has shown promising therapeutic potential against DN,...

Metabolomic signatures of brain aging: A multimodal and genetic study.

Accelerated brain aging is increasingly recognized as a transdiagnostic risk factor for neuropsychiatric and neurodegenerative disorders, yet its meta...

Frailty index and type 2 diabetes with renal complications: insights from Mendelian randomization and retrospective observational study.

OBJECTIVES: To investigate the relationship between frailty and diabetic kidney disease (DKD)-related renal complications using Mendelian randomizatio...

Recent advances in point-of-care colorimetric biosensors for detecting small molecule metabolites.

In the context of the growing need for decentralized diagnostics, the colorimetric method emerges as an ideal candidate for point-of-care (POC) applic...

Multi-omics investigation of per- and polyfluoroalkyl substances in lung adenocarcinoma: comprehensive network toxicology, machine learning and molecular docking experiments.

Growing concerns regarding per- and polyfluoroalkyl substances (PFAS) as pervasive environmental contaminants have prompted increasing scrutiny regard...

Precision Nutrition and Chronic Disease: Integrating Genomics, Microbiome, and Digital Health for Personalized Dietary Interventions.

Chronic non-communicable diseases (NCDs), including cardiovascular disease, type 2 diabetes, obesity, and metabolic syndrome, account for more than 70...

Quantifying the Economic Value of AI-Assisted Diabetic Retinopathy Screening: A Meta-Analysis Using the Incremental Net Benefit Framework.

OBJECTIVE: To quantitatively synthesize the economic value of artificial intelligence (AI)-assisted screening for diabetic retinopathy (DR), a leading...

Privacy-aware diabetic retinopathy grading and visual lesion-focused interpretability through mixture-of-experts federated deep learning with explainable AI.

Diabetic Retinopathy (DR) is still a major cause of vision loss that can be avoided. This means that we need automated screening systems that can work...

An interpretable machine learning model for diabetic foot risk classification in patients with diabetes.

Diabetic foot is a severe chronic complication of diabetes, mainly resulting from peripheral neuropathy and vasculopathy, and may progress to ulcers, ...

APASA: adaptive selection of informative peritumoral regions for improved automated cancer lesion analysis.

Precise cancer lesion analysis in medical imaging critically depends on the accurate definition of regions of interest (ROIs), which directly influenc...

Interpretability of multimodal neural networks for prediction of visual acuity in patients with branch retinal vein occlusion.

Branch retinal vein occlusion (BRVO) can cause persistent visual impairment, and predicting long-term best-corrected visual acuity (BCVA) after anti-v...

Cardiometabolic multimorbidity and atrial fibrillation: insights from traditional statistical and artificial intelligence approaches.

BACKGROUND: Cardiometabolic multimorbidity (CMM) is increasingly prevalent among patients with atrial fibrillation (AF), yet its independent impact on...

Clinically accessible eye-tracking in thyroid-associated ophthalmopathy: exploratory classification modeling and gaze allocation to disease-related periocular images.

BACKGROUND: Thyroid-associated ophthalmopathy (TAO) is an autoimmune orbital disease that may cause ocular motility abnormalities, altered periocular ...

Integrating neurobiological markers to prospectively predict adolescent non-suicidal self-injury and suicide attempts: a machine learning approach.

BACKGROUND: Suicide attempts (SA) and non-suicidal self-injury (NSSI) are closely related phenomena, both common in adolescent clinical samples. This ...

The Value of a Predictive Model Based on Multimodal Ultrasound Imaging Biomarkers Combined with Clinical Features in the Diagnosis of Thyroid Nodules.

INTRODUCTION: This study aimed to develop and validate diagnostic models for distinguishing benign and malignant thyroid nodules. METHODS: Between Jan...

Development and Validation of Machine-Learning-Based Prediction Models for Thyroid Diseases During Pregnancy.

BACKGROUND International guidelines recommend early screening based on targeted risk factors to identify thyroid disease during pregnancy. The complex...

Super learner model for predicting carotid plaque regression in type 2 diabetes on PCSK9 inhibitors.

OBJECTIVES: Evidence regarding the effects of proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors on carotid plaque regression in patient...

Integrating artificial intelligence and machine learning into risk prediction for type 2 diabetes: A model for early identification of high-risk populations.

Family history, body mass index (BMI), and ethnicity are three key, well-established determinants of susceptibility to type 2 diabetes mellitus (T2DM)...