AIMC Topic: Arthroplasty, Replacement, Knee
Clear Filters Showing 121 to 130 of 210 articles
Robot-assisted total knee arthroplasty is associated with a learning curve for surgical time but not for component alignment, limb alignment and gap balancing.
Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA
Nov 3, 2020
PURPOSE: The application of robotics in the operating theatre for total knee arthroplasty (TKA) remains controversial. As with all new technology, the introduction of new systems is associated with a learning curve and potentially associated with ext...
Artificial intelligence and surgical innovation: lower limb arthroplasty.
British journal of hospital medicine (London, England : 2005)
Oct 6, 2020
The number of patients requiring hip and knee arthroplasty continues to rise each year. Patients are living longer and expecting to remain active into later life following joint replacement. Developments in computer-assisted surgery and robotic techn...
Deep Learning in Gait Parameter Prediction for OA and TKA Patients Wearing IMU Sensors.
Sensors (Basel, Switzerland)
Sep 28, 2020
Quantitative assessments of patient movement quality in osteoarthritis (OA), specifically spatiotemporal gait parameters (STGPs), can provide in-depth insight into gait patterns, activity types, and changes in mobility after total knee arthroplasty (...
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...
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).
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 utilization efficiency using machine learning techniques: A study of heterogeneity in preoperative healthcare utilization among super-utilizers.
American journal of surgery
Jan 27, 2020
INTRODUCTION: In the United States, 5% of patients represent up to 55% of all health care costs. This study sought to define healthcare utilization patterns among super-utilizers, as well as assess possible variation in patient outcomes.
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