AIMC Topic: Knee Joint

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Auto-segmentation of the tibia and femur from knee MR images via deep learning and its application to cartilage strain and recovery.

Journal of biomechanics
The ability to efficiently and reproducibly generate subject-specific 3D models of bone and soft tissue is important to many areas of musculoskeletal research. However, methodologies requiring such models have largely been limited by lengthy manual s...

Deep Learning Reconstruction Enables Prospectively Accelerated Clinical Knee MRI.

Radiology
Background MRI is a powerful diagnostic tool with a long acquisition time. Recently, deep learning (DL) methods have provided accelerated high-quality image reconstructions from undersampled data, but it is unclear if DL image reconstruction can be r...

Prediction of knee adduction moment using innovative instrumented insole and deep learning neural networks in healthy female individuals.

The Knee
BACKGROUND: The knee adduction moment, a biomechanical risk factor of knee osteoarthritis, is typically measured in a gait laboratory with expensive equipment and inverse dynamics modeling software. We aimed to develop a framework for a portable knee...

Accuracy of Advanced Active Robot for Total Knee Arthroplasty: A Cadaveric Study.

The journal of knee surgery
Although the accuracy of other types of robotic systems for total knee arthroplasty (TKA) has been assessed in cadaveric studies, no investigations have been performed to evaluate this newly advanced active robotic system. Therefore, the authors aime...

Acceleration of knee magnetic resonance imaging using a combination of compressed sensing and commercially available deep learning reconstruction: a preliminary study.

BMC medical imaging
PURPOSE: To evaluate whether deep learning reconstruction (DLR) accelerates the acquisition of 1.5-T magnetic resonance imaging (MRI) knee data without image deterioration.

A fully automatic target detection and quantification strategy based on object detection convolutional neural network YOLOv3 for one-step X-ray image grading.

Analytical methods : advancing methods and applications
Methods for automatic image analysis are demanded for dealing with the explosively increased imaging data in clinics. Osteoarthritis (OA) is a typical disease diagnosed based on X-ray imaging. Herein, we propose a novel modeling strategy based on YOL...

Quantitative Gait Feature Assessment on Two-Dimensional Body Axis Projection Planes Converted from Three-Dimensional Coordinates Estimated with a Deep Learning Smartphone App.

Sensors (Basel, Switzerland)
To assess pathological gaits quantitatively, three-dimensional coordinates estimated with a deep learning model were converted into body axis plane projections. First, 15 healthy volunteers performed four gait patterns; that is, normal, shuffling, sh...

In slope-changing osteotomy one millimeter is not one degree: results of an artificial intelligence-automated software analysis.

International orthopaedics
BACKGROUND: Anterior closing wedge osteotomies (ACWO) are performed in revision anterior cruciate ligament (ACL) surgery to correct an excessive posterior tibial slope (PTS).

A More Posterior Tibial Tubercle (Decreased Sagittal Tibial Tubercle-Trochlear Groove Distance) Is Significantly Associated With Patellofemoral Joint Degenerative Cartilage Change: A Deep Learning Analysis.

Arthroscopy : the journal of arthroscopic & related surgery : official publication of the Arthroscopy Association of North America and the International Arthroscopy Association
PURPOSE: To perform patellofemoral joint (PFJ) geometric measurements on knee magnetic resonance imaging scans and determine their relations with chondral lesions in a multicenter cohort using deep learning.