AIMC Topic: Osteoarthritis, Knee
Clear Filters Showing 141 to 150 of 223 articles
Enabling early detection of osteoarthritis from presymptomatic cartilage texture maps via transport-based learning.
Proceedings of the National Academy of Sciences of the United States of America
Sep 21, 2020
Many diseases have no visual cues in the early stages, eluding image-based detection. Today, osteoarthritis (OA) is detected after bone damage has occurred, at an irreversible stage of the disease. Currently no reliable method exists for OA detection...
Comment on "Robot-Assisted versus Conventional Total and Unicompartmental Knee Arthroplasty: A Meta-analysis of Radiological and Functional Outcomes".
The journal of knee surgery
Aug 24, 2020
Prediction of Total Knee Replacement and Diagnosis of Osteoarthritis by Using Deep Learning on Knee Radiographs: Data from the Osteoarthritis Initiative.
Radiology
Jun 23, 2020
Background The methods for assessing knee osteoarthritis (OA) do not provide enough comprehensive information to make robust and accurate outcome predictions. Purpose To develop a deep learning (DL) prediction model for risk of OA progression by usin...
Serum adipokines/related inflammatory factors and ratios as predictors of infrapatellar fat pad volume in osteoarthritis: Applying comprehensive machine learning approaches.
Scientific reports
Jun 19, 2020
OBJECTIVE: The infrapatellar fat pad (IPFP) has been associated with knee osteoarthritis onset and progression. This study uses machine learning (ML) approaches to predict serum levels of some adipokines/related inflammatory factors and their ratios ...
Multi-classifier prediction of knee osteoarthritis progression from incomplete imbalanced longitudinal data.
Scientific reports
May 21, 2020
Conventional inclusion criteria used in osteoarthritis clinical trials are not very effective in selecting patients who would benefit from a therapy being tested. Typically majority of selected patients show no or limited disease progression during a...
Automating three-dimensional osteoarthritis histopathological grading of human osteochondral tissue using machine learning on contrast-enhanced micro-computed tomography.
Osteoarthritis and cartilage
May 11, 2020
OBJECTIVE: To develop and validate a machine learning (ML) approach for automatic three-dimensional (3D) histopathological grading of osteochondral samples imaged with contrast-enhanced micro-computed tomography (CEμCT).
Development of automatic measurement for patellar height based on deep learning and knee radiographs.
European radiology
Apr 23, 2020
OBJECTIVES: To develop and evaluate the performance of a deep learning-based system for automatic patellar height measurements using knee radiographs.
Letter to the Editor on Robot-Assisted Total Knee Arthroplasty Does Not Improve Long-Term Clinical and Radiologic Outcomes.
The Journal of arthroplasty
Apr 17, 2020
Deep Learning Predicts Total Knee Replacement from Magnetic Resonance Images.
Scientific reports
Apr 14, 2020
Knee Osteoarthritis (OA) is a common musculoskeletal disorder in the United States. When diagnosed at early stages, lifestyle interventions such as exercise and weight loss can slow OA progression, but at later stages, only an invasive option is avai...
Assessment of knee pain from MR imaging using a convolutional Siamese network.
European radiology
Feb 13, 2020
OBJECTIVES: It remains difficult to characterize the source of pain in knee joints either using radiographs or magnetic resonance imaging (MRI). We sought to determine if advanced machine learning methods such as deep neural networks could distinguis...
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