Arthritis

Leveraging deep learning to infer continuous predictions from ordinal labels in medical imaging.

In clinical medicine, variables like disease severity are often categorized into discrete ordinal labels such as normal/mild/moderate/severe. However, these labels, commonly used to train and evaluate disease severity prediction models, simplify an underlying continuous severity spectrum. Using continuous scores can aid in detecting small severity changes more sensitively over time. We introduce a...

Overcoming barriers to delivery of biologics by novel approaches and, advanced delivery systems.

Biologics is emerging as fastest growing field of drug development offering specificity and high therapeutic potential. However, their therapeutic efficacy is dependent on their route of administration and targeted delivery. Unlike small molecules, biologics face stability, permeability, solubility and immunogenicity challenges due to their inherent features. Hence tailored approaches are required...

Machine Learning Using Preoperative Patient Factors Can Predict the Severity of Pain Following Primary Total Hip Arthroplasty.

BACKGROUND: Persistent postoperative pain is a challenge in total hip arthroplasty (THA), yet a lack of accurate predictive tools exists for this outc...

SaintGSE: Transformer-based efficient and explainable gene set enrichment analysis.

OBJECTIVE: Gene set enrichment analysis (GSEA) is widely used to interpret genome-wide expression data, but pathway inference from differentially expr...

Time-to-event risk prediction of dual sensory loss in middle-aged patients with symptomatic knee osteoarthritis: model development, preliminary external validation, and explainability analysis.

BACKGROUND: Knee osteoarthritis (KOA) is a highly prevalent chronic condition that substantially impairs functional capacity and quality of life among...

LMO7-mediated ubiquitination of SIRT3 promotes osteoarthritis progression: an investigation using machine learning and molecular dynamics simulations.

BACKGROUND: This study aims to inform clinical decision-making by identifying metabolism-related biomarkers involved in the progression of osteoarthri...

Imaging Immune Heterogeneity in Nasopharyngeal Carcinoma: A Multidimensional Radiomics Perspective.

RATIONALE AND OBJECTIVES: Nasopharyngeal carcinoma (NPC) is characterized by a distinctive virologic and immunologic profile, in which Epstein-Barr vi...

Minimum joint space width demonstrates higher inter-and intra-observer reliability than Kellgren-Lawrence grading in knee osteoarthritis.

PURPOSE: The purpose of this systematic review was to compare the inter- and intra-observer reliability of Kellgren-Lawrence (KL) grading versus minim...

Sharing a whole-/total-body [18F]FDG-PET/CT dataset with CT-derived segmentations: an ENHANCE.PET initiative.

We present a large whole-body and total-body curated dataset of dual-modality 2-deoxy-2-[18F]fluoro-D-glucose (FDG)-Positron Emission Tomography/Compu...

Elucidating the Role of Oxidative Stress-Associated Genes FKBP Prolyl Isomerase 5 in Osteoarthritis Development and Immunological Milieu.

Inferring rheumatoid arthritis disease activity status from the electronic health records across health systems.

OBJECTIVE: Disease activity plays a central role in rheumatoid arthritis (RA) clinical studies. The inconsistent availability of data on disease activ...

An interpretable machine learning tool for rheumatoid arthritis screening: integrating novel cellular morphological parameters with routine blood count indices.

BACKGROUND: Rheumatoid arthritis (RA) is a chronic autoimmune disease that can cause significant disability. Early detection is vital for improving ou...

Integrating Gait Analysis and AI Into Knee Osteoarthritis Care: Protocol for a 3-Phase Participatory Qualitative Study.

BACKGROUND: Knee osteoarthritis (OA) leads to pain, disability, and reduced quality of life. For advanced stages, knee arthroplasty surgery is the sta...

Bone Magnetic Resonance Imaging: From Conventional Methods to AI-Driven Solutions.

With global population aging and increased life expectancy, bone diseases affect a substantial proportion of individuals worldwide, imposing a signifi...

Imaging and genomics in stroke.

Imaging after ischemic and hemorrhagic stroke may allow measurement of key phenotypes of injury and recovery for which targeted therapies are still la...

Using causal machine learning and real world data to improve dose response decision making for secukinumab in psoriatic arthritis.

Personalized treatment in psoriatic arthritis (PsA) remains challenging, particularly in guiding dose escalation decisions. We applied a causal machin...

Baseline pain, fatigue, and sleep quality predict 12-week pain improvement in inflammatory arthritis: retrospective real-world analysis of a digital health application cohort.

UNLABELLED: Despite advances in pharmacotherapy, many patients with inflammatory arthritis (IA) experience residual pain even in remission. Digital he...

Bibliometric and visual analysis of artificial intelligence-related research in osteoarthritis: Trends and frontiers (2008-2025).

Osteoarthritis (OA) causes joint pain, functional impairment, and significantly impacts patients' quality of life. Recent advancements in artificial i...

Identification and validation of KLRB1-related biomarkers in rheumatoid arthritis.

BACKGROUND: Rheumatoid arthritis (RA) is a systemic autoimmune inflammatory disorder. KLRB1 (killer cell lectin like receptor B1), which is intricatel...

Integrative analysis of single-cell and bulk RNA sequencing data using machine learning identifies PTGDS as a key subsynovial fibroblast gene in osteoarthritis.