Diabetes

Beyond Accuracy, SHAP, and Anchors -- On the difficulty of designing effective end-user explanations

Modern machine learning produces models that are impossible for users or developers to fully understand -- raising concerns about trust, oversight and human dignity. Transparency and explainability methods aim to provide some help in understanding models, but it remains challenging for developers to design explanations that are understandable to target users and effective for their purpose. Emer...

Object Detection for Medical Image Analysis: Insights from the RT-DETR Model

Deep learning has emerged as a transformative approach for solving complex pattern recognition and object detection challenges. This paper focuses on the application of a novel detection framework based on the RT-DETR model for analyzing intricate image data, particularly in areas such as diabetic retinopathy detection. Diabetic retinopathy, a leading cause of vision loss globally, requires accu...

Integrating Probabilistic Trees and Causal Networks for Clinical and Epidemiological Data

Healthcare decision-making requires not only accurate predictions but also insights into how factors influence patient outcomes. While traditional M...

Flexible Blood Glucose Control: Offline Reinforcement Learning from Human Feedback

Reinforcement learning (RL) has demonstrated success in automating insulin dosing in simulated type 1 diabetes (T1D) patients but is currently unabl...

Adaptive Class Learning to Screen Diabetic Disorders in Fundus Images of Eye

The prevalence of ocular illnesses is growing globally, presenting a substantial public health challenge. Early detection and timely intervention ar...

Predicting Diabetic Retinopathy Using a Machine Learning Approach Informed by Whole-Exome Sequencing Studies.

OBJECTIVE: To establish and validate a novel diabetic retinopathy (DR) risk-prediction model using a whole-exome sequencing (WES)-based machine learni...

An Integrated Approach to AI-Generated Content in e-health

Artificial Intelligence-Generated Content, a subset of Generative Artificial Intelligence, holds significant potential for advancing the e-health se...

AI-Driven Diabetic Retinopathy Screening: Multicentric Validation of AIDRSS in India

Purpose: Diabetic retinopathy (DR) is a major cause of vision loss, particularly in India, where access to retina specialists is limited in rural ar...

Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends

The emergence of artificial intelligence (AI), particularly deep learning (DL), has marked a new era in the realm of ophthalmology, offering transfo...

Diabetic Retinopathy Detection Using CNN with Residual Block with DCGAN

Diabetic Retinopathy (DR) is a major cause of blindness worldwide, caused by damage to the blood vessels in the retina due to diabetes. Early detect...

Implications of Artificial Intelligence on Health Data Privacy and Confidentiality

The rapid integration of artificial intelligence (AI) in healthcare is revolutionizing medical diagnostics, personalized medicine, and operational e...

Host Microenvironment Reprogramming by Saccharides Overcomes Lung Barriers for mRNA Therapeutics

Overcoming biological barriers remains the paramount challenge for pulmonary mRNA therapeutics. Conventional approaches focus exclusively on passively...

Machine Learning-Based Bioactivity Prediction of Potential PPAR-γ Agonists for the Management of Diabetes

This research paper presents a machine learning approach to predict bioactivity of compounds that can act as PPAR-gamma agonist, a critical target for...

Deep Learning for Predicting Stem Cell Efficiency for use in Beta Cell Differentiation

Recent clinical trial data have shown that cell therapy holds curative potential for type-1 diabetes, however the large amounts of lab-grown cells req...

Non-segmented unsupervised learning of multispectral whole slide images for robust analysis of tissue repair and regeneration

Analyzing whole tissue architecture remains challenging due to the inherent complexity of multicellular organization, variable morphology, and the lim...

Comparative Machine Learning Analysis of Saliva and Plaque Microbiomes in Children with Type 1 Diabetes

Type 1 diabetes (T1D) is associated with microbial dysbiosis. While most research has focused on the gut microbiome, limited data address the role of ...

Whole tissue spatial cellular analysis reveals increased macrophage infiltration in pancreata of autoantibody positive donors and patients with type 1 diabetes

While extensive efforts have characterized lymphoid populations that contribute to pancreatic ‘insulitis’ in type 1 diabetes, significant gaps remain ...

Single-cell proteomics of pancreatic islet cells reveals type 1 diabetes and donor-specific features

Type 1 diabetes mellitus (T1DM) is the most common severe chronic disease in children and adolescents and requires life-long exogenous insulin treatme...

Supervised machine learning identifies impaired mitochondrial quality control in β cells with development of type 2 diabetes

In type 2 diabetes (T2D), molecular pathways driving β cell failure are difficult to resolve with standard single cell analysis. Here we developed an ...

MAP-PRS: Multi-Ancestry Portfolio-Based Polygenic Risk Scores

Polygenic Risk Scores (PRS) are emerging tools for predicting an individual’s genetic risk for complex diseases. However, their usefulness in clinical...