Diabetes

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 devices. By collecting large amounts of input-output data, where inputs represent system states and outputs are MPC-generated control actions, NNs can be trained to replicate MPC behavior at a fraction of the computational cost. However, although the...

[Current applications and challenges of artificial intelligence in diabetes management].

In recent years, the rapid development of artificial intelligence (AI) has brought innovative opportunities to diabetes management, with significant application potential in various aspects such as prevention, screening, diagnosis, and treatment of diabetes. Through big data analysis, AI can achieve precise diabetes sub-typing and further promote personalized treatment. In terms of complication sc...

Attention GhostUNet++: Enhanced Segmentation of Adipose Tissue and Liver in CT Images

Accurate segmentation of abdominal adipose tissue, including subcutaneous (SAT) and visceral adipose tissue (VAT), along with liver segmentation, is...

Integrating Vision and Location with Transformers: A Multimodal Deep Learning Framework for Medical Wound Analysis

Effective recognition of acute and difficult-to-heal wounds is a necessary step in wound diagnosis. An efficient classification model can help wound...

Progressive Transfer Learning for Multi-Pass Fundus Image Restoration

Diabetic retinopathy is a leading cause of vision impairment, making its early diagnosis through fundus imaging critical for effective treatment pla...

GlyTwin: Digital Twin for Glucose Control in Type 1 Diabetes Through Optimal Behavioral Modifications Using Patient-Centric Counterfactuals

Frequent and long-term exposure to hyperglycemia (i.e., high blood glucose) increases the risk of chronic complications such as neuropathy, nephropa...

Beyond Glucose-Only Assessment: Advancing Nocturnal Hypoglycemia Prediction in Children with Type 1 Diabetes

The dead-in-bed syndrome describes the sudden and unexplained death of young individuals with Type 1 Diabetes (T1D) without prior long-term complica...

Multi-modal and Multi-view Fundus Image Fusion for Retinopathy Diagnosis via Multi-scale Cross-attention and Shifted Window Self-attention

The joint interpretation of multi-modal and multi-view fundus images is critical for retinopathy prevention, as different views can show the complet...

Mobile-Driven Incentive Based Exercise for Blood Glucose Control in Type 2 Diabetes

We propose and create an incentive based recommendation algorithm aimed at improving the lifestyle of diabetic patients. This algorithm is integrate...

Enhancing Metabolic Syndrome Prediction with Hybrid Data Balancing and Counterfactuals

Metabolic Syndrome (MetS) is a cluster of interrelated risk factors that significantly increases the risk of cardiovascular diseases and type 2 diab...

A Geometric-Aware Perspective and Beyond: Hybrid Quantum-Classical Machine Learning Methods

Geometric Machine Learning (GML) has shown that respecting non-Euclidean geometry in data spaces can significantly improve performance over naive Eu...

WoundAmbit: Bridging State-of-the-Art Semantic Segmentation and Real-World Wound Care

Chronic wounds affect a large population, particularly the elderly and diabetic patients, who often exhibit limited mobility and co-existing health ...

Diabetic Retinopathy Detection Based on Convolutional Neural Networks with SMOTE and CLAHE Techniques Applied to Fundus Images

Diabetic retinopathy (DR) is one of the major complications in diabetic patients' eyes, potentially leading to permanent blindness if not detected t...

Maternal and Fetal Health Status Assessment by Using Machine Learning on Optical 3D Body Scans

Monitoring maternal and fetal health during pregnancy is crucial for preventing adverse outcomes. While tests such as ultrasound scans offer high ac...

Improving Early Prediction of Type 2 Diabetes Mellitus with ECG-DiaNet: A Multimodal Neural Network Leveraging Electrocardiogram and Clinical Risk Factors

Type 2 Diabetes Mellitus (T2DM) remains a global health challenge, underscoring the need for early and accurate risk prediction. This study presents...

Early detection of diabetes through transfer learning-based eye (vision) screening and improvement of machine learning model performance and advanced parameter setting algorithms

Diabetic Retinopathy (DR) is a serious and common complication of diabetes, caused by prolonged high blood sugar levels that damage the small retina...

Machine learning reveals connections between preclinical type 2 diabetes subtypes and brain health.

Previous research has established type 2 diabetes mellitus as a significant risk factor for various disorders, adversely impacting human health. While...

Machine Learning Prediction of Cardiovascular Risk in Type 1 Diabetes Mellitus Using Radiomics Features from Multimodal Retinal Images

This study aimed to develop a machine learning (ML) algorithm capable of determining cardiovascular risk in multimodal retinal images from patients ...

Implementation of A New, Mobile Diabetic Retinopathy Screening Model Incorporating Artificial Intelligence in Remote Western Australia.

OBJECTIVE: Diabetic retinopathy (DR) screening rates are poor in remote Western Australia where communities rely on outdated primary care-based retina...

Relationships Between Retinal Vascular Characteristics and Systemic Indicators in Patients With Diabetes Mellitus.

PURPOSE: To develop a deep learning method for vessel segmentation in fundus images, measure retinal vessels, and study the connection between retinal...