AIMC Topic: Osteoarthritis

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Comparative ranking of marginal confounding impact of natural language processing-derived versus structured features in pharmacoepidemiology.

Computers in biology and medicine
OBJECTIVE: To explore the ability of natural language processing (NLP) methods to identify confounder information beyond what can be identified using claims codes alone for pharmacoepidemiology.

Interpretable machine learning and radiomics in hip MRI diagnostics: comparing ONFH and OA predictions to experts.

Frontiers in immunology
PURPOSE: Distinguishing between Osteonecrosis of the femoral head (ONFH) and Osteoarthritis (OA) can be subjective and vary between users with different backgrounds and expertise. This study aimed to construct and evaluate several Radiomics-based mac...

Identification of WDR74 and TNFRSF12A as biomarkers for early osteoarthritis using machine learning and immunohistochemistry.

Frontiers in immunology
BACKGROUND: Osteoarthritis (OA) is a chronic joint condition that causes pain, limited mobility, and reduced quality of life, posing a threat to healthy aging. Early detection is crucial for improving prognosis. Recent research has focused on the rol...

Visualizing Preosteoarthritis: Updates on UTE-Based Compositional MRI and Deep Learning Algorithms.

Journal of magnetic resonance imaging : JMRI
Osteoarthritis (OA) is heterogeneous and involves structural changes in the whole joint, such as cartilage, meniscus/labrum, ligaments, and tendons, mainly with short T2 relaxation times. Detecting OA before the onset of irreversible changes is cruci...

Development and validation of a new nomogram for self-reported OA based on machine learning: a cross-sectional study.

Scientific reports
Developing a new diagnostic prediction model for osteoarthritis (OA) to assess the likelihood of individuals developing OA is crucial for the timely identification of potential populations of OA. This allows for further diagnosis and intervention, wh...

Developing the new diagnostic model by integrating bioinformatics and machine learning for osteoarthritis.

Journal of orthopaedic surgery and research
BACKGROUND: Osteoarthritis (OA) is a common cause of disability among the elderly, profoundly affecting quality of life. This study aims to leverage bioinformatics and machine learning to develop an artificial neural network (ANN) model for diagnosin...

Understanding the role of machine learning in predicting progression of osteoarthritis.

The bone & joint journal
AIMS: Machine learning (ML), a branch of artificial intelligence that uses algorithms to learn from data and make predictions, offers a pathway towards more personalized and tailored surgical treatments. This approach is particularly relevant to prev...

Automatic detection of temporomandibular joint osteoarthritis radiographic features using deep learning artificial intelligence. A Diagnostic accuracy study.

Journal of stomatology, oral and maxillofacial surgery
OBJECTIVE: The purpose of this study was to investigate the diagnostic performance of a neural network Artificial Intelligence model for the radiographic confirmation of Temporomandibular Joint Osteoarthritis in reference to an experienced radiologis...

Ensemble approach for predicting the diagnosis of osteoarthritis using physical activity factors.

Journal of evaluation in clinical practice
BACKGROUND: Osteoarthritis (OA) is a common degenerative disease of the joints. Risk factors for OA include non-modifiable factors such as age and sex, as well as modifiable factors like physical activity.

Machine learning algorithm-based biomarker exploration and validation of mitochondria-related diagnostic genes in osteoarthritis.

PeerJ
The role of mitochondria in the pathogenesis of osteoarthritis (OA) is significant. In this study, we aimed to identify diagnostic signature genes associated with OA from a set of mitochondria-related genes (MRGs). First, the gene expression profiles...