Diabetes

Comprehensive Evaluation of OCT-based Automated Segmentation of Retinal Layer, Fluid and Hyper-Reflective Foci: Impact on Diabetic Retinopathy Severity Assessment

Diabetic retinopathy (DR) is a leading cause of vision loss, requiring early and accurate assessment to prevent irreversible damage. Spectral Domain Optical Coherence Tomography (SD-OCT) enables high-resolution retinal imaging, but automated segmentation performance varies, especially in cases with complex fluid and hyperreflective foci (HRF) patterns. This study proposes an active-learning-base...

Artificial Intelligence Versus Rules-Based Approach for Segmenting NonPerfusion Area in a DRCR Retina Network Optical Coherence Tomography Angiography Dataset.

PURPOSE: Loss of retinal perfusion is associated with both onset and worsening of diabetic retinopathy (DR). Optical coherence tomography angiography is a noninvasive method for measuring the nonperfusion area (NPA) and has promise as a scalable screening tool. This study compares two optical coherence tomography angiography algorithms for quantifying NPA.

Using gut microbiome metagenomic hypervariable features for diabetes screening and typing through supervised machine learning.

Diabetes mellitus is a complex metabolic disorder and one of the fastest-growing global public health concerns. The gut microbiota is implicated in th...

Personalized Causal Graph Reasoning for LLMs: A Case Study on Dietary Recommendations

Large Language Models (LLMs) effectively leverage common-sense knowledge for general reasoning, yet they struggle with personalized reasoning when t...

Explainable, Multi-modal Wound Infection Classification from Images Augmented with Generated Captions

Infections in Diabetic Foot Ulcers (DFUs) can cause severe complications, including tissue death and limb amputation, highlighting the need for accu...

RURANET++: An Unsupervised Learning Method for Diabetic Macular Edema Based on SCSE Attention Mechanisms and Dynamic Multi-Projection Head Clustering

Diabetic Macular Edema (DME), a prevalent complication among diabetic patients, constitutes a major cause of visual impairment and blindness. Althou...

GONet: A Generalizable Deep Learning Model for Glaucoma Detection

Glaucomatous optic neuropathy (GON) is a prevalent ocular disease that can lead to irreversible vision loss if not detected early and treated. The t...

A graph neural network-based multispectral-view learning model for diabetic macular ischemia detection from color fundus photographs

Diabetic macular ischemia (DMI), marked by the loss of retinal capillaries in the macular area, contributes to vision impairment in patients with di...

Interpretable Retinal Disease Prediction Using Biology-Informed Heterogeneous Graph Representations

Interpretability is crucial to enhance trust in machine learning models for medical diagnostics. However, most state-of-the-art image classifiers ba...

Type 1 Diabetes Management using GLIMMER: Glucose Level Indicator Model with Modified Error Rate

Managing Type 1 Diabetes (T1D) demands constant vigilance as individuals strive to regulate their blood glucose levels to avert the dangers of dysgl...

SmartEdge: Smart Healthcare End-to-End Integrated Edge and Cloud Computing System for Diabetes Prediction Enabled by Ensemble Machine Learning

The Internet of Things (IoT) revolutionizes smart city domains such as healthcare, transportation, industry, and education. The Internet of Medical ...

Machine Learning for Everyone: Simplifying Healthcare Analytics with BigQuery ML

Machine learning (ML) transforms healthcare by enabling predictive analytics, personalized treatments, and improved patient outcomes. However, tradi...

Is an Ultra Large Natural Image-Based Foundation Model Superior to a Retina-Specific Model for Detecting Ocular and Systemic Diseases?

The advent of foundation models (FMs) is transforming medical domain. In ophthalmology, RETFound, a retina-specific FM pre-trained sequentially on 1...

Semantic Data Augmentation Enhanced Invariant Risk Minimization for Medical Image Domain Generalization

Deep learning has achieved remarkable success in medical image classification. However, its clinical application is often hindered by data heterogen...

Controllable Sequence Editing for Counterfactual Generation

Sequence models generate counterfactuals by modifying parts of a sequence based on a given condition, enabling reasoning about "what if" scenarios. ...

Can metformin prevent cancer relative to sulfonylureas? A target trial emulation accounting for competing risks and poor overlap via double/debiased machine learning estimators.

There is mounting interest in the possibility that metformin, indicated for glycemic control in type 2 diabetes, has a range of additional beneficial ...

PNL: a software to build polygenic risk scores using a super learner approach based on PairNet, a Convolutional Neural Network.

SUMMARY: Polygenic risk scores (PRSs) hold promise for early disease diagnosis and personalized treatment, but their overall discriminative power rema...

ASSESSMENT OF ARTIFICIAL INTELLIGENCE SOFTWARE FOR AUTOMATIC SCREENING OF DIABETIC RETINOPATHY BASED ON FUNDUS PHOTOGRAPHS IN MELANODERM SUBJECTS.

PURPOSE: To assess the Gaiha Prio Retino +™ Artificial Intelligence (AI) software for detecting diabetic retinopathy (DR).

Non-Linear Dose-Response Relationship for Metformin in Japanese Patients With Type 2 Diabetes: Analysis of Irregular Longitudinal Data by Interpretable Machine Learning Models.

The dose-response relationship between metformin and change in hemoglobin A1c (HbA1c) shows a maximum at 1500-2000 mg/day in patients with type 2 diab...

Towards Transparent and Accurate Diabetes Prediction Using Machine Learning and Explainable Artificial Intelligence

Diabetes mellitus (DM) is a global health issue of significance that must be diagnosed as early as possible and managed well. This study presents a ...