Arthritis

Predicting Depression in Canadians with or at Risk of Diabetes: A Cross-Sectional Machine Learning Analysis

Depression often goes unrecognized in individuals at risk or living with diabetes, presenting considerable challenges for primary care clinicians. Although large language models and other foundation model approaches are drawing significant attention, we systematically compared six established machine learning algorithms-Logistic Regression, Random Forest, AdaBoost, XGBoost, Naive Bayes, and Artifi...

Automating Imaging Biomarker Analysis for Knee Osteoarthritis Using an Open-Source MRI-Based Deep Learning Pipeline

Osteoarthritis (OA) is a leading cause of chronic disability worldwide, with knee OA being the most prevalent form. Quantitative assessment of knee joint tissues using Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) has the potential to enhance OA diagnosis and progression tracking. However, current methodologies for segmenting and extracting quantitative metrics from knee ...

CMANet: Cross-Modal Attention Network for 3-D Knee MRI and Report-Guided Osteoarthritis Assessment

Knee osteoarthritis (OA) is a leading cause of disability worldwide, with early identification of structural changes critical for improving patient ou...

Development of a novel musculoskeletal hypothesis using sparse Group Factor Analysis: the ADVANCE cohort

Musculoskeletal conditions are a leading global cause of disability, yet the factors influencing long-term musculoskeletal health, particularly follow...

Serum metabolic signatures are associated with anti-drug antibody development in rheumatoid arthritis patients treated with adalimumab

Development of anti-drug antibodies (ADAs) is a barrier to long-term efficacy of biologic therapies in rheumatoid arthritis (RA), but no biomarkers ex...

Epigenetic patient stratification reveals a sub-endotype of type 2 asthma with altered B-cell response

Despite biomarker-guided treatment strategies, clinical outcomes among patients with type 2 (T2)-high asthma remain heterogeneous, with some patients ...

Deep learning-based precision phenotyping of spine curvature identifies novel genetic risk loci for scoliosis in the UK Biobank

Scoliosis is the most common developmental spinal deformity, but its genetic underpinnings remain only partially understood. To enhance the identifica...

Policy gradient-guided ensemble learning for enhanced polygenic risk prediction in ultra-high-dimensional genomics

Polygenic diseases challenge genetic risk prediction due to extreme dimensionality, low per-variant effect sizes, and non-additive interactions. Conve...

Robust Disease Prognosis via Diagnostic Knowledge Preservation: A Sequential Learning Approach

Accurate disease prognosis is essential for patient care but is often hindered by the lack of long-term data. This study explores deep learning traini...

Optimizing Dose-Response Decisions in Psoriatic Arthritis via Causal Machine Learning: A Real-World Evaluation of Secukinumab Treatment

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

Inferring rheumatoid arthritis disease activity status from the electronic health records across health systems to enable real-world data studies

Disease activity plays a central role in rheumatoid arthritis (RA) clinical studies. However, RA disease activity is inconsistently recorded in real-w...

Scaling genetic discovery for organ volumes using machine learning-assisted imputation and bias-corrected GWAS

MRI-derived organ and tissue volumes are powerful endophenotypes for studying complex disease, but their availability is limited by cost and throughpu...

Identification and validation of biomarkers FAM107A, RHOBTB1 and ZBTB16 associated with dietary restriction in osteoarthritis

Osteoarthritis (OA), a chronic ailment that leads to joint degeneration, could be prevented by dietary restriction (DR). This study investigated the m...

Non-invasive epidermal proteomics and machine learning permits molecular subclassification of psoriasis and eczematous dermatitis

Current approaches to selecting molecularly targeted therapies (biologics and oral small molecules) for immune-mediated skin diseases largely overlook...

Mechanosensitive TRPV4 immunohistochemistry improves deep learning-based grading of ductal carcinoma in situ beyond H&E morphology

Ductal carcinoma in situ (DCIS) is a non-invasive breast cancer spanning a biologic continuum from atypical ductal hyperplasia (ADH) to high-grade les...

autoscoRA: Deep Learning to Automate Sharp/van der Heijde Scoring of Radiographic Damage in Rheumatoid Arthritis

Regular imaging by conventional radiography to assess for joint damage is a cornerstone in the management of rheumatoid arthritis (RA). Scoring system...

Clustering and classification of early knee osteoarthritis using machine-learning analysis of step-up and down test kinematics in recreational table tennis players.

OBJECTIVE: Early detection of knee osteoarthritis is crucial for improving patient outcomes. While conventional imaging methods often fail to detect e...

Prediction of Anti-rheumatoid Arthritis Natural Products of Xanthocerais Lignum Based on LC-MS and Artificial Intelligence.

AIMS: Employing the technique of liquid chromatography-mass spectrometry (LCMS) in conjunction with artificial intelligence (AI) technology to predict...

Can temporomandibular joint osteoarthritis be diagnosed on MRI proton density-weighted images with diagnostic support from the latest deep learning classification models?

OBJECTIVES: This study aimed to clarify the performance of MRI-based deep learning classification models in diagnosing temporomandibular joint osteoar...

KneeXNeT: An Ensemble-Based Approach for Knee Radiographic Evaluation

Knee osteoarthritis (OA) is the most common joint disorder and a leading cause of disability. Diagnosing OA severity typically requires expert asses...