AIMC Topic: Knee Joint

Clear Filters Showing 241 to 250 of 396 articles

Using Deep Learning to Accelerate Knee MRI at 3 T: Results of an Interchangeability Study.

AJR. American journal of roentgenology
Deep learning (DL) image reconstruction has the potential to disrupt the current state of MRI by significantly decreasing the time required for MRI examinations. Our goal was to use DL to accelerate MRI to allow a 5-minute comprehensive examination ...

Artificial intelligence and surgical innovation: lower limb arthroplasty.

British journal of hospital medicine (London, England : 2005)
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...

The optimisation of deep neural networks for segmenting multiple knee joint tissues from MRIs.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Automated semantic segmentation of multiple knee joint tissues is desirable to allow faster and more reliable analysis of large datasets and to enable further downstream processing e.g. automated diagnosis. In this work, we evaluate the use of condit...

Continuous Estimation of Knee Joint Angle Based on Surface Electromyography Using a Long Short-Term Memory Neural Network and Time-Advanced Feature.

Sensors (Basel, Switzerland)
Continuous joint angle estimation based on a surface electromyography (sEMG) signal can be used to improve the man-machine coordination performance of the exoskeleton. In this study, we proposed a time-advanced feature and utilized long short-term me...

Mechanics of walking and running up and downhill: A joint-level perspective to guide design of lower-limb exoskeletons.

PloS one
Lower-limb wearable robotic devices can improve clinical gait and reduce energetic demand in healthy populations. To help enable real-world use, we sought to examine how assistance should be applied in variable gait conditions and suggest an approach...

Interpretability of Input Representations for Gait Classification in Patients after Total Hip Arthroplasty.

Sensors (Basel, Switzerland)
Many machine learning models show black box characteristics and, therefore, a lack of transparency, interpretability, and trustworthiness. This strongly limits their practical application in clinical contexts. For overcoming these limitations, Explai...

Iterative Learning Control for a Soft Exoskeleton with Hip and Knee Joint Assistance.

Sensors (Basel, Switzerland)
Walking on different terrains leads to different biomechanics, which motivates the development of exoskeletons for assisting on walking according to the type of a terrain. The design of a lightweight soft exoskeleton that simultaneously assists multi...

Prediction of Total Knee Replacement and Diagnosis of Osteoarthritis by Using Deep Learning on Knee Radiographs: Data from the Osteoarthritis Initiative.

Radiology
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
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 ...