AIMC Topic: Knee Injuries

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MR Protocol Optimization With Deep Learning: A Proof of Concept.

Current problems in diagnostic radiology
PURPOSE: This study was performed to demonstrate that a properly trained convolutional neural net (CNN) can provide an acceptable surrogate for human readers when performing a protocol optimization study. Tears of the anterior cruciate ligament (ACL)...

On-field player workload exposure and knee injury risk monitoring via deep learning.

Journal of biomechanics
In sports analytics, an understanding of accurate on-field 3D knee joint moments (KJM) could provide an early warning system for athlete workload exposure and knee injury risk. Traditionally, this analysis has relied on captive laboratory force plate...

Deep Learning Approach for Evaluating Knee MR Images: Achieving High Diagnostic Performance for Cartilage Lesion Detection.

Radiology
Purpose To determine the feasibility of using a deep learning approach to detect cartilage lesions (including cartilage softening, fibrillation, fissuring, focal defects, diffuse thinning due to cartilage degeneration, and acute cartilage injury) wit...

Assessment of knee laxity using a robotic testing device: a comparison to the manual clinical knee examination.

Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA
PURPOSE: The purpose of this study was to collect knee laxity data using a robotic testing device. The data collected were then compared to the results obtained from manual clinical examination.

Design and control of an active 1-DoF mechanism for knee rehabilitation.

Disability and rehabilitation. Assistive technology
A 1-DoF robot is designed and fabricated to be used for knee rehabilitation training. The mechanism (robot) is designed to perform specific set of exercises while the patient is sitting on a chair. The therapy process for patients has different stage...

Arthroscopy-validated diagnostic performance of sub-5-min deep learning super-resolution 3T knee MRI in children and adolescents.

Skeletal radiology
OBJECTIVE: This study aims to determine the diagnostic performance of sub-5-min combined sixfold parallel imaging (PIx3)-simultaneous multislice (SMSx2)-accelerated deep learning (DL) super-resolution 3T knee MRI in children and adolescents.

Deep Learning Superresolution for Simultaneous Multislice Parallel Imaging-Accelerated Knee MRI Using Arthroscopy Validation.

Radiology
Background Deep learning (DL) methods can improve accelerated MRI but require validation against an independent reference standard to ensure robustness and accuracy. Purpose To validate the diagnostic performance of twofold-simultaneous-multislice (S...

A Deep Learning System for Synthetic Knee Magnetic Resonance Imaging: Is Artificial Intelligence-Based Fat-Suppressed Imaging Feasible?

Investigative radiology
MATERIALS AND METHODS: This single-center study was approved by the institutional review board. Artificial intelligence-based FS MRI scans were created from non-FS images using a deep learning system with a modified convolutional neural network-based...

Can Machine-learning Algorithms Predict Early Revision TKA in the Danish Knee Arthroplasty Registry?

Clinical orthopaedics and related research
BACKGROUND: Revision TKA is a serious adverse event with substantial consequences for the patient. As the demand for TKA rises, reducing the risk of revision TKA is becoming increasingly important. Predictive tools based on machine-learning algorithm...