AIMC Topic: Knee

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A Joint Group Sparsity-based deep learning for multi-contrast MRI reconstruction.

Journal of magnetic resonance (San Diego, Calif. : 1997)
Multi-contrast magnetic resonance imaging (MRI) can provide richer diagnosis information. The data acquisition time, however, is increased than single-contrast imaging. To reduce this time, k-space undersampling is an effective way but a smart recons...

Joint mechanical properties estimation with a novel EMG-based knee rehabilitation robot: A machine learning approach.

Medical engineering & physics
Joint dynamic properties play essential roles in a wide range of biomechanical movement control. This paper develops a device with a novel mechatronic design to apply small-amplitude perturbations to the human knee. Surface Electromyography is employ...

DSMENet: Detail and Structure Mutually Enhancing Network for under-sampled MRI reconstruction.

Computers in biology and medicine
Reconstructing zero-filled MR images (ZF) from partial k-space by convolutional neural networks (CNN) is an important way to accelerate MRI. However, due to the lack of attention to different components in ZF, it is challenging to learn the mapping f...

Validity of an artificial intelligence, human pose estimation model for measuring single-leg squat kinematics.

Journal of biomechanics
Few studies have investigated the validity of 2D pose estimation models to evaluate kinematics throughout a motion and none have included adolescents. Adolescent athletes completed single-leg squats while 3D kinematic data and 2D sagittal and frontal...

Successful real-world application of an osteoarthritis classification deep-learning model using 9210 knees-An orthopedic surgeon's view.

Journal of orthopaedic research : official publication of the Orthopaedic Research Society
This study aimed to evaluate the performance of a deep-learning model to evaluate knee osteoarthritis using Kellgren-Lawrence grading in real-life knee radiographs. A deep convolutional neural network model was trained using 8964 knee radiographs fro...

Pediatric age estimation from radiographs of the knee using deep learning.

European radiology
OBJECTIVES: Age estimation, especially in pediatric patients, is regularly used in different contexts ranging from forensic over medicolegal to clinical applications. A deep neural network has been developed to automatically estimate chronological ag...

Inferring pediatric knee skeletal maturity from MRI using deep learning.

Skeletal radiology
PURPOSE: Many children who undergo MR of the knee to evaluate traumatic injury may not undergo a separate dedicated evaluation of their skeletal maturity, and we wished to investigate how accurately skeletal maturity could be automatically inferred f...

Open Source Software for Automatic Subregional Assessment of Knee Cartilage Degradation Using Quantitative T2 Relaxometry and Deep Learning.

Cartilage
OBJECTIVE: We evaluated a fully automated femoral cartilage segmentation model for measuring T2 relaxation values and longitudinal changes using multi-echo spin-echo (MESE) magnetic resonance imaging (MRI). We open sourced this model and developed a ...

Which GAN? A comparative study of generative adversarial network-based fast MRI reconstruction.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
Fast magnetic resonance imaging (MRI) is crucial for clinical applications that can alleviate motion artefacts and increase patient throughput. -space undersampling is an obvious approach to accelerate MR acquisition. However, undersampling of -space...

DL-MRI: A Unified Framework of Deep Learning-Based MRI Super Resolution.

Journal of healthcare engineering
Magnetic resonance imaging (MRI) is widely used in the detection and diagnosis of diseases. High-resolution MR images will help doctors to locate lesions and diagnose diseases. However, the acquisition of high-resolution MR images requires high magne...