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Knee

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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...

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 ...

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...

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...

[Study on the accuracy of automatic segmentation of knee CT images based on deep learning].

Zhongguo xiu fu chong jian wai ke za zhi = Zhongguo xiufu chongjian waike zazhi = Chinese journal of reparative and reconstructive surgery
OBJECTIVE: To develop a neural network architecture based on deep learning to assist knee CT images automatic segmentation, and validate its accuracy.

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...

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...

[A generative adversarial network-based unsupervised domain adaptation method for magnetic resonance image segmentation].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
Intelligent medical image segmentation methods have been rapidly developed and applied, while a significant challenge is domain shift. That is, the segmentation performance degrades due to distribution differences between the source domain and the ta...

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...