AIMC Topic: Knee Injuries
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Deep Learning-Based Multimodal 3 T MRI for the Diagnosis of Knee Osteoarthritis.
Computational and mathematical methods in medicine
Apr 29, 2022
The objective of this study was to investigate the application effect of deep learning model combined with different magnetic resonance imaging (MRI) sequences in the evaluation of cartilage injury of knee osteoarthritis (KOA). Specifically, an image...
Deep Learning-Based Magnetic Resonance Imaging Image Features for Diagnosis of Anterior Cruciate Ligament Injury.
Journal of healthcare engineering
Jul 2, 2021
To study and explore the adoption value of magnetic resonance imaging (MRI) in the diagnosis of anterior cruciate ligament (ACL) injuries, a multimodal feature fusion model based on deep learning was proposed for MRI diagnosis. After the related perf...
Artificial Intelligence Image-Assisted Knee Ligament Trauma Repair Efficacy Analysis and Postoperative Femoral Nerve Block Analgesia Effect Research.
World neurosurgery
Nov 27, 2020
OBJECTIVE: To analyze artificial intelligence image-assisted knee ligament injury repair and femoral nerve block analgesia after surgery.
Humans
Artificial Intelligence
Machine Learning
Anterior Cruciate Ligament Reconstruction
Knee Injuries
Image Processing, Computer-Assisted
Magnetic Resonance Imaging
Male
Pain, Postoperative
Middle Aged
Femoral Nerve
Femoral Artery
Adolescent
Female
Nerve Block
Angiography
Aged
Ligaments
Young Adult
Anterior Cruciate Ligament Injuries
Adult
Using Deep Learning to Accelerate Knee MRI at 3 T: Results of an Interchangeability Study.
AJR. American journal of roentgenology
Oct 14, 2020
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 ...
New Machine Learning Approach for Detection of Injury Risk Factors in Young Team Sport Athletes.
International journal of sports medicine
Sep 13, 2020
The purpose of this article is to present how predictive machine learning methods can be utilized for detecting sport injury risk factors in a data-driven manner. The approach can be used for finding new hypotheses for risk factors and confirming the...
The Use of Artificial Intelligence in the Evaluation of Knee Pathology.
Seminars in musculoskeletal radiology
Jan 28, 2020
Artificial intelligence (AI) holds the potential to revolutionize the field of radiology by increasing the efficiency and accuracy of both interpretive and noninterpretive tasks. We have only just begun to explore AI applications in the diagnostic ev...
MR Protocol Optimization With Deep Learning: A Proof of Concept.
Current problems in diagnostic radiology
Oct 21, 2019
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
Jul 8, 2019
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
Jul 31, 2018
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
Dec 24, 2015
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.
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