Arthritis

Layer Separation: Adjustable Joint Space Width Images Synthesis in Conventional Radiography

Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by joint inflammation and progressive structural damage. Joint space width (JSW) is a critical indicator in conventional radiography for evaluating disease progression, which has become a prominent research topic in computer-aided diagnostic (CAD) systems. However, deep learning-based radiological CAD systems for JSW analysi...

Pre-trained convolutional neural network with transfer learning by artificial illustrated images classify power Doppler ultrasound images of rheumatoid arthritis joints.

OBJECTIVE: To study the classification performance of a pre-trained convolutional neural network (CNN) with transfer learning by artificial joint ultrasonography images in rheumatoid arthritis (RA).

VidSole: A Multimodal Dataset for Joint Kinetics Quantification and Disease Detection with Deep Learning

Understanding internal joint loading is critical for diagnosing gait-related diseases such as knee osteoarthritis; however, current methods of measu...

Foundations of a Knee Joint Digital Twin from qMRI Biomarkers for Osteoarthritis and Knee Replacement

This study forms the basis of a digital twin system of the knee joint, using advanced quantitative MRI (qMRI) and machine learning to advance precis...

COX-2 Inhibitor Prediction With KNIME: A Codeless Automated Machine Learning-Based Virtual Screening Workflow.

Cyclooxygenase-2 (COX-2) is an enzyme that plays a crucial role in inflammation by converting arachidonic acid into prostaglandins. The overexpression...

Hengqin-RA-v1: Advanced Large Language Model for Diagnosis and Treatment of Rheumatoid Arthritis with Dataset based Traditional Chinese Medicine

Large language models (LLMs) primarily trained on English texts, often face biases and inaccuracies in Chinese contexts. Their limitations are prono...

snATAC-Express infers Gene Expression from Prioritized Chromatin Accessibility Peaks using Machine Learning

Single cell multi-omic investigation opens-up new opportunities to understand mechanisms of gene regulation. Existing methods for inferring transcript...

Powerful and accurate case-control analysis of spatial molecular data

As spatial molecular data grow in scope and resolution, there is a pressing need to identify key spatial structures associated with disease. Current a...

MechanoAge, a machine learning platform to identify individuals susceptible to breast cancer based on mechanical properties of single cells

Existing breast cancer risk models inadequately identify individuals at latent risk, particularly among women without known genetic mutations or famil...

multiDEGGs: a multi-omic differential network analysis package for biomarker discovery and predictive modeling

Modern clinical trials increasingly leverage high-throughput omic data for patient stratification and biomarker discovery. While traditional different...

JointMap identified SERPINA3+ chondrocytes as a therapeutic target for osteoarthritis

Osteoarthritis (OA) is a prevalent degenerative joint disease that affects nearly half of the population aged over 65. Recent single-cell transcriptio...

Multi-omics integration at cell type resolution uncovers gene-metabolite mechanisms underlying osteoarthritis heterogeneity

Metabolic dysregulation is an important factor for osteoarthritis pathogenesis, but comprehensive studies of underlying mechanisms and pathways are ra...

Accuracy and Scalability of Machine Learning Methods for Genotype-Phenotype Association Data

Many machine learning methods can be applied to predicting phenotypes from genetic data. Which of these methods work best remains an open question, ho...

Targeted Enzymatic Fragmentation of Lipoprotein(a) via Kringle IV Domains: A Clearance-Enhancing Therapeutic Strategy for Cardiovascular Disease

Elevated lipoprotein(a) [Lp(a)] is an independent, genetically determined risk factor for atherosclerotic cardiovascular disease (ASCVD). Its unique a...

Lectin Microarray-based Glycomics and Machine Learning Identify Shared Osteoarthritis Biomarkers in Humans, Dogs, and Horses

Post-traumatic osteoarthritis (PTOA) is a common sequela to joint injury in both humans and companion animal species such as horses and dogs. Despite ...

A validated set of neural gene reporter mice and chemical tracers tools for mapping knee innervating neurons

Joint pain is an increasing concern for our aging population, as current therapies to slow joint disease progression or reduce pain are largely ineffe...

PULSAR: a Foundation Model for Multi-scale and Multicellular Biology

Biology emerges from interactions across physical scales, where molecular interactions drive cellular states, which in turn orchestrate multicellular ...

Integrating Machine Learning with Musculoskeletal Simulation Improves OpenCap Video-Based Dynamics Estimation

Musculoskeletal dynamics influence the progression and rehabilitation of many movement-related conditions. However, accurately estimating whole-body d...

Quantitative AI based on evolutionary computation and information theory combined with mathematical functions yields algorithmic RNA biomarkers from a randomized rheumatoid arthritis clinical trial, accurately predicting individual patient responses to anti-TNF treatment, to enable precision medicine

The routine derivation of novel biomarkers from therapeutic clinical trials to accurately predict individual patient’s responses, would be a significa...

Characterisation of 3000 patient reported outcomes with predictive machine learning to develop a scientific platform to study fatigue in Inflammatory Bowel Disease

Fatigue is commonly identified by IBD patients as major issue that affects their wellbeing. This presentation, however, is complex, multifactorial and...