Diabetes

Evaluating Large Language Models for Multimodal Simulated Ophthalmic Decision-Making in Diabetic Retinopathy and Glaucoma Screening

Large language models (LLMs) can simulate clinical reasoning based on natural language prompts, but their utility in ophthalmology is largely unexplored. This study evaluated GPT-4's ability to interpret structured textual descriptions of retinal fundus photographs and simulate clinical decisions for diabetic retinopathy (DR) and glaucoma screening, including the impact of adding real or synthet...

Research on Improving the High Precision and Lightweight Diabetic Retinopathy Detection of YOLOv8n

Early detection and diagnosis of diabetic retinopathy is one of the current research focuses in ophthalmology. However, due to the subtle features of micro-lesions and their susceptibility to background interference, ex-isting detection methods still face many challenges in terms of accuracy and robustness. To address these issues, a lightweight and high-precision detection model based on the im...

Sparse group LASSO and nonlinear machine learning for frequency-feature optimization in noninvasive blood glucose monitoring via bioimpedance spectroscopy.

Diabetic patients need to test their blood glucose levels (BGL) frequently; however, traditional methods of blood collection and testing cause great p...

Research Gaps, Challenges, and Opportunities in Automated Insulin Delivery Systems.

Since the discovery of the life-saving hormone insulin in 1921 by Dr. Frederick Banting in 1921, there have been many critical discoveries and technic...

Equitable Deep Learning for Diabetic Retinopathy Detection Using Multidimensional Retinal Imaging With Fair Adaptive Scaling.

PURPOSE: To investigate the fairness of existing deep models for diabetic retinopathy (DR) detection and introduce an equitable model to reduce group ...

Deep learning-enhanced image analysis for liquid crystal optical sensing.

In liquid crystal (LC) sensors, each microliter of LC contains billions of molecules with numerous orientation combinations, generating thousands of o...

MACHINE LEARNING AND SHOCK INDICES-DERIVED SCORE FOR PREDICTING CONTRAST-INDUCED NEPHROPATHY IN ACUTE CORONARY SYNDROME PATIENTS.

Background: Contrast-induced nephropathy (CIN) is a serious complication following acute coronary syndrome (ACS), leading to increased morbidity and m...

Neural Networks for On-Chip Model Predictive Control: A Method to Build Optimized Training Datasets and its Application to Type-1 Diabetes.

Training neural networks (NNs) to behave as model predictive control (MPC) algorithms is an effective way to implement them in constrained embedded de...

Recommendations for the Management of Diabetes During Ramadan Applying the Principles of the ADA/ EASD Consensus: Update 2025.

Ramadan fasting is a sacred ritual observed by approximately 1.8 billion Muslims each year, most of whom adhere to fasting due to its significance as ...

Integrative analyses of 16S rDNA sequencing and serum metabolomics demonstrate significant roles for the oral microbiota and serum metabolites in post-kidney transplant diabetes mellitus.

Oral microbiota and serum metabolites play crucial roles in diabetes, but their relationship with post-transplant diabetes mellitus (PTDM), a common c...

The Use of Continuous Glucose Monitoring to Diagnose Stage 2 Type 1 Diabetes.

This consensus report evaluates the potential role of continuous glucose monitoring (CGM) in screening for stage 2 type 1 diabetes (T1D). CGM offers a...

Development and validation of a convenient dementia risk prediction tool for diabetic population: A large and longitudinal machine learning cohort study.

BACKGROUND: Diabetes mellitus has been shown to increase the risk of dementia, with diabetic patients demonstrating twice the dementia incidence rate ...

MIP-based electrochemical sensor with machine learning for accurate ZIKV detection in protein- and glucose-rich urine.

Nowadays, a multitude of biosensors are being developed worldwide. However, a significant challenge arises when these biosensors are tested in real sa...

Risk prediction models for patients with recurrent diabetic foot ulcers: A systematic review.

OBJECTIVES: To systematically review published studies on risk prediction models for patients with recurrent diabetic foot ulcers.

CoMMiT: Co-informed inference of microbiome-metabolome interactions via transfer learning

Recent multi-omic microbiome studies enable integrative analysis of microbes and metabolites, uncovering their associations with various host condit...

Visual-Conversational Interface for Evidence-Based Explanation of Diabetes Risk Prediction

Healthcare professionals need effective ways to use, understand, and validate AI-driven clinical decision support systems. Existing systems face two...

FundaQ-8: A Clinically-Inspired Scoring Framework for Automated Fundus Image Quality Assessment

Automated fundus image quality assessment (FIQA) remains a challenge due to variations in image acquisition and subjective expert evaluations. We in...

Federated Learning for MRI-based BrainAGE: a multicenter study on post-stroke functional outcome prediction

$\textbf{Objective:}$ Brain-predicted age difference (BrainAGE) is a neuroimaging biomarker reflecting brain health. However, training robust BrainA...

Comparative Analysis of Deep Learning Strategies for Hypertensive Retinopathy Detection from Fundus Images: From Scratch and Pre-trained Models

This paper presents a comparative analysis of deep learning strategies for detecting hypertensive retinopathy from fundus images, a central task in ...

Diabetes Prediction and Management Using Machine Learning Approaches

Diabetes has emerged as a significant global health issue, especially with the increasing number of cases in many countries. This trend Underlines t...