AIMC Topic: Knee Joint

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Lower Limb Torque Prediction for Sit-To-Walk Strategies Using Long Short-Term Memory Neural Networks.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Joint torque prediction is crucial when investigating biomechanics, evaluating treatments, and designing powered assistive devices. Controllers in assistive technology require reference torque trajectories to set the level of assistance for a patient...

Optimizing knee osteoarthritis severity prediction on MRI images using deep stacking ensemble technique.

Scientific reports
Knee osteoarthritis (KOA) represents a well-documented degenerative arthropathy prevalent among the elderly population. KOA is a persistent condition, also referred to as progressive joint Disease, stemming from the continual deterioration of cartila...

Uncertainty-Aware Deep Learning Characterization of Knee Radiographs for Large-Scale Registry Creation.

The Journal of arthroplasty
BACKGROUND: We present an automated image ingestion pipeline for a knee radiography registry, integrating a multilabel image-semantic classifier with conformal prediction-based uncertainty quantification and an object detection model for knee hardwar...

Evaluation of a novel robotic testing method for stability and kinematics of total knee arthroplasty.

Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA
PURPOSE: This work developed a novel preclinical test of total knee replacements (TKRs) in order to explain TKR instability linked to patient dissatisfaction. It was hypothesized that stability tests on the isolated moving prostheses would provide no...

MRI deep learning models for assisted diagnosis of knee pathologies: a systematic review.

European radiology
OBJECTIVES: Despite showing encouraging outcomes, the precision of deep learning (DL) models using different convolutional neural networks (CNNs) for diagnosis remains under investigation. This systematic review aims to summarise the status of DL MRI...

A Robotic Clamped-Kinematic System to Study Knee Ligament Injury.

Annals of biomedical engineering
Knee ligament injury is among the most common sports injuries and is associated with long recovery periods and low return-to-sport rates. Unfortunately, the mechanics of ligament injury are difficult to study in vivo, and computational studies provid...

Recurrent Neural Network Enabled Continuous Motion Estimation of Lower Limb Joints From Incomplete sEMG Signals.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Decoding continuous human motion from surface electromyography (sEMG) in advance is crucial for improving the intelligence of exoskeleton robots. However, incomplete sEMG signals are prevalent on account of unstable data transmission, sensor malfunct...

High accuracy in lower limb alignment analysis using convolutional neural networks, with improvements needed for joint-level metrics.

Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA
PURPOSE: Evaluation of long-leg standing radiographs (LSR) is a standardised procedure for analysis of primary or secondary deformities of the lower limbs. Deep-learning convolutional neural networks (CNN) offer the potential to enhance radiological ...

Research on the Motion Control Strategy of a Lower-Limb Exoskeleton Rehabilitation Robot Using the Twin Delayed Deep Deterministic Policy Gradient Algorithm.

Sensors (Basel, Switzerland)
The motion control system of a lower-limb exoskeleton rehabilitation robot (LLERR) is designed to assist patients in lower-limb rehabilitation exercises. This research designed a motion controller for an LLERR-based on the Twin Delayed Deep Determini...

Learning co-plane attention across MRI sequences for diagnosing twelve types of knee abnormalities.

Nature communications
Multi-sequence magnetic resonance imaging is crucial in accurately identifying knee abnormalities but requires substantial expertise from radiologists to interpret. Here, we introduce a deep learning model incorporating co-plane attention across imag...