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Osteoarthritis

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Epidemiology of osteoarthritis: literature update 2022-2023.

Current opinion in rheumatology
PURPOSE OF REVIEW: This review highlights recently published studies on osteoarthritis (OA) epidemiology, including topics related to understudied populations and joints, imaging, and advancements in artificial intelligence (AI) methods.

Rapid screening for autoimmune diseases using Fourier transform infrared spectroscopy and deep learning algorithms.

Frontiers in immunology
INTRODUCE: Ankylosing spondylitis (AS), rheumatoid arthritis (RA), and osteoarthritis (OA) are three rheumatic immune diseases with many common characteristics. If left untreated, they can lead to joint destruction and functional limitation, and in s...

Machine Learning Approaches to the Prediction of Osteoarthritis Phenotypes and Outcomes.

Current rheumatology reports
PURPOSE OF REVIEW: Osteoarthritis (OA) is a complex heterogeneous disease with no effective treatments. Artificial intelligence (AI) and its subfield machine learning (ML) can be applied to data from different sources to (1) assist clinicians and pat...

Deep learning discrimination of rheumatoid arthritis from osteoarthritis on hand radiography.

Skeletal radiology
PURPOSE: To develop a deep learning model to distinguish rheumatoid arthritis (RA) from osteoarthritis (OA) using hand radiographs and to evaluate the effects of changing pretraining and training parameters on model performance.

An artificial intelligence model for the radiographic diagnosis of osteoarthritis of the temporomandibular joint.

Scientific reports
The interpretation of the signs of Temporomandibular joint (TMJ) osteoarthritis on cone-beam computed tomography (CBCT) is highly subjective that hinders the diagnostic process. The objectives of this study were to develop and test the performance of...

Artificial intelligence in osteoarthritis detection: A systematic review and meta-analysis.

Osteoarthritis and cartilage
OBJECTIVES: As an increasing number of studies apply artificial intelligence (AI) algorithms in osteoarthritis (OA) detection, we performed a systematic review and meta-analysis to pool the data on diagnostic performance metrics of AI, and to compare...

Development and Validation of a Deep-Learning Model to Predict Total Hip Replacement on Radiographs: The Total Hip Replacement Prediction (THREP) Model.

The Journal of bone and joint surgery. American volume
BACKGROUND: There are few methods for accurately assessing the risk of total hip arthroplasty (THA) in patients with osteoarthritis. A novel and reliable method that could play a substantial role in research and clinical routine should be investigate...

Deep Learning Augmented Osteoarthritis Grading Standardization.

Tissue engineering. Part A
Manual grading of cartilage histology images for investigating the extent and severity of osteoarthritis (OA) involves critical examination of the cell characteristics, which makes this task tiresome, tedious, and error prone. This results in wide in...

The Diagnostic Features of Peripheral Blood Biomarkers in Identifying Osteoarthritis Individuals: Machine Learning Strategies and Clinical Evidence.

Current computer-aided drug design
BACKGROUND: People with osteoarthritis place a huge burden on society. Early diagnosis is essential to prevent disease progression and to select the best treatment strategy more effectively. In this study, the aim was to examine the diagnostic featur...

Direct anterior approach with conventional instruments versus robotic posterolateral approach in elective total hip replacement for primary osteoarthritis: a case-control study.

Journal of orthopaedics and traumatology : official journal of the Italian Society of Orthopaedics and Traumatology
BACKGROUND: The purpose of this study is to compare peri-operative and short-term outcomes in patients who underwent elective total hip replacement (THA) for primary osteoarthritis (OA) with direct anterior approach (DAA) versus a pair-matched cohort...