Endocrinology

Diabetes

Latest AI and machine learning research in diabetes for healthcare professionals.

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Correcting heterogeneous diagnostic bias when developing clinical prediction models using causal hidden Markov models

In routine care, individuals identified a priori as high-risk are usually tested for conditions more...

Retina-RAG: Retrieval-Augmented Vision-Language Modeling for Joint Retinal Diagnosis and Clinical Report Generation

Diabetic Retinopathy (DR) is a leading cause of preventable blindness among working-age adults world...

Conserved neuroectodermal aging encodes primate health and longevity

Neuroectoderm-derived tissues are highly metabolically active and exhibit minimal regenerative turno...

Combinatorial epigenomic patterns define regulatory programs underlying disease heterogeneity

Disease is a heterogeneous process that involves multiple organs and cell types. Understanding how g...

OphMAE: Bridging Volumetric and Planar Imaging with a Foundation Model for Adaptive Ophthalmological Diagnosis

The advent of foundation models has heralded a new era in medical artificial intelligence (AI), enab...

A Digital Twin for Tracking and Forecasting Glycemia with Septic Patients in ICUs

We present a digital twin framework for real time glucose monitoring and forecasting in septic patie...

Dual GLP-1/FGF21 agonism suppresses voluntary alcohol consumption, alcohol choice, and nucleus accumbens dopamine modulation

Excessive alcohol consumption remains a major public health challenge with limited therapeutic optio...

From Prediction to Practice: A Task-Aware Evaluation Framework for Blood Glucose Forecasting

Clinical time-series forecasting is increasingly studied for decision support, yet standard aggregat...

One Size Fits All? Comparing Foundation and Task-specific Models for Retinal Fluid Segmentation

Retinal fluids, detectable through optical coherence tomography (OCT), are key biomarkers for retina...

Development of Explainable Machine Learning Framework for Early Detection and Risk Stratification of Diabetes in Age Specific Variations

Objective To develop and evaluate a novel machine learning (ML) framework tailored to a clinical dia...

An empirical evaluation of the risks of AI model updates using clinical data: stability, arbitrariness, and fairness

Artificial Intelligence and Machine Learning (AI/ML) models used in clinical settings are increasing...

Impact of Age Specialized Models for Hypoglycemia Classification

Disease progression varies with age and is influenced by underlying genetic, biochemical, and hormon...

Liver Biomarker Improves AHA/ACC 10-year ASCVD Risk Prediction in US and China Cohorts with ML

Introduction: Accurate stratification of hard atherosclerotic cardiovascular disease (ASCVD) risk re...

Robust Diabetic Retinopathy Grading Using Dual-Resolution Attention-Based Deep Learning with Ordinal Regression

Diabetic retinopathy (DR) is a leading cause of vision impairment worldwide, and automated grading s...

Imbalance-Aware Optimal Transport Learning for Cost-Effective Diabetic Retinopathy Screening

Abstract Background Diabetic Retinopathy (DR) is one of the leading cause of vision loss and blindne...

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