Results 41 to 50 of about 432,246 (250)

A Machine Learning Model to Predict Knee Osteoarthritis Cartilage Volume Changes over Time Using Baseline Bone Curvature

open access: yesBiomedicines, 2022
The hallmark of osteoarthritis (OA), the most prevalent musculoskeletal disease, is the loss of cartilage. By using machine learning (ML), we aimed to assess if baseline knee bone curvature (BC) could predict cartilage volume loss (CVL) at one year, and ...
Hossein Bonakdari   +3 more
doaj   +1 more source

Future directions for the management of pain in osteoarthritis. [PDF]

open access: yes, 2014
Osteoarthritis (OA) is the predominant form of arthritis worldwide, resulting in a high degree of functional impairment and reduced quality of life owing to chronic pain. To date, there are no treatments that are known to modify disease progression of OA
Aigner T   +23 more
core   +1 more source

Automated Segmentation of Knee MRI Using Hierarchical Classifiers and Just Enough Interaction Based Learning: Data from Osteoarthritis Initiative [PDF]

open access: yesMed Image Comput Comput Assist Interv. 2016 Oct;9901:344-351. doi: 10.1007/978-3-319-46723-8_40. Epub 2016 Oct 2, 2019
We present a fully automated learning-based approach for segmenting knee cartilage in the presence of osteoarthritis (OA). The algorithm employs a hierarchical set of two random forest classifiers. The first is a neighborhood approximation forest, the output probability map of which is utilized as a feature set for the second random forest (RF ...
arxiv   +1 more source

Osteoarthritis and the rule of halves [PDF]

open access: yes, 2014
<b>Background</b> Symptomatic osteoarthritis poses a major challenge to primary health care but no studies have related accessing primary care ("detection"), receiving recommended treatments ("treatment"), and achieving adequate control ...
B.I. Nicholl   +31 more
core   +1 more source

A patients’ view of OA: the Global Osteoarthritis Patient Perception Survey (GOAPPS), a pilot study

open access: yesBMC Musculoskeletal Disorders, 2020
Background Globally, osteoarthritis (OA) is the third condition associated with disability. There is still poor treatment in OA but science holds the key to finding better treatments and a cure.
Marianna Vitaloni   +19 more
doaj   +1 more source

Oral herbal therapies for treating osteoarthritis (review) [PDF]

open access: yes, 2014
Background Medicinal plant products are used orally for treating osteoarthritis. Although their mechanisms of action have not yet been elucidated in full detail, interactions with common inflammatory mediators provide a rationale for using them to ...
Cameron, Melainie, Chrubasik, Sigrun
core   +1 more source

Walking time measures for evaluating OA of the knee

open access: yesSouth African Journal of Physiotherapy, 1994
This study assessed the test-retest reliability and the sensitivity of self-paced walking time measurements for evaluating the functional performance of persons with knee osteoarthritis (OA).
R. Marks
doaj   +1 more source

Automated Detection of Patellofemoral Osteoarthritis from Knee Lateral View Radiographs Using Deep Learning: Data from the Multicenter Osteoarthritis Study (MOST) [PDF]

open access: yesarXiv, 2021
Objective: To assess the ability of imaging-based deep learning to predict radiographic patellofemoral osteoarthritis (PFOA) from knee lateral view radiographs. Design: Knee lateral view radiographs were extracted from The Multicenter Osteoarthritis Study (MOST) (n = 18,436 knees).
arxiv  

Colony housing promotes structural and functional changes during surgically induced osteoarthritis in rats

open access: yesOsteoarthritis and Cartilage Open, 2020
Summary: Objective: The aim of the study was to investigate how social housing with high locomotion activity affects experimental osteoarthritis (OA) in rats.
C. Brenneis   +5 more
doaj  

Geodesic analysis in Kendall's shape space with epidemiological applications [PDF]

open access: yesJournal of Mathematical Imaging and Vision 62(4):549--559, 2020, 2019
We analytically determine Jacobi fields and parallel transports and compute geodesic regression in Kendall's shape space. Using the derived expressions, we can fully leverage the geometry via Riemannian optimization and thereby reduce the computational expense by several orders of magnitude over common, nonlinear constrained approaches. The methodology
arxiv   +1 more source

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