AIMC Topic: Knee Joint

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Comparison of lower limb kinematic and kinetic estimation during athlete jumping between markerless and marker-based motion capture systems.

Scientific reports
Markerless motion capture (ML) systems, which utilize deep learning algorithms, have significantly expanded the applications of biomechanical analysis. Jump tests are now essential tools for athlete monitoring and injury prevention. However, the vali...

Prediction of coronal alignment in robotic-assisted total knee arthroplasty with artificial intelligence.

The Knee
INTRODUCTION: Robotic-assisted technologies provide the ability to avoid soft tissue release by utilizing more accurate bony cuts during total knee arthroplasty (TKA). However, the ideal limb alignment is not yet established. The aim of this study wa...

Dynamic simulation of knee joint mechanics: individualized multi-moment finite element modelling of patellar tendon stress during landing.

Journal of biomechanics
Patellar tendinopathy is prevalent in sports requiring high jumping demands, and understanding the in vivo biomechanical behavior of the patellar tendon (PT) during landing is crucial for developing effective injury prevention and rehabilitation stra...

Classification of Grades of Subchondral Sclerosis from Knee Radiographic Images Using Artificial Intelligence.

Sensors (Basel, Switzerland)
Osteoarthritis (OA) is the most common joint disease, affecting over 300 million people worldwide. Subchondral sclerosis is a key indicator of OA. Currently, the diagnosis of subchondral sclerosis is primarily based on radiographic images; however, r...

Use of Artificial Intelligence on Imaging and Preoperatory Planning of the Knee Joint: A Scoping Review.

Medicina (Kaunas, Lithuania)
: This scoping review explores the current state of the art of AI-based applications in the field of orthopedics, focusing on its implementation in diagnostic imaging and preoperative planning of knee joint procedures. : The search was carried out us...

Comparison of Ground Reaction Forces and Net Joint Moment Predictions: Skeletal Model Versus Artificial Neural Network-Based Approach.

Journal of applied biomechanics
Artificial neural networks (ANNs) are becoming a regular tool to support biomechanical methods, while physics-based models are widespread to understand the mechanics of body in motion. Thus, this study aimed to demonstrate the accuracy of recurrent A...

Forecasting motion trajectories of elbow and knee joints during infant crawling based on long-short-term memory (LSTM) networks.

Biomedical engineering online
BACKGROUND: Hands-and-knees crawling is a promising rehabilitation intervention for infants with motor impairments, while research on assistive crawling devices for rehabilitation training was still in its early stages. In particular, precisely gener...

Evaluating potential for AI automation of quantitative and semi-quantitative MRI scoring in arthritis, especially at the knee: a systematic literature review.

Skeletal radiology
OBJECTIVE: This systematic review explores key quantitative and semi-quantitative MRI-based scoring systems for arthritis biomarkers, focusing on their potential for automation through AI.

Kinetic Pattern Recognition in Home-Based Knee Rehabilitation Using Machine Learning Clustering Methods on the Slider Digital Physiotherapy Device: Prospective Observational Study.

JMIR formative research
BACKGROUND: Recent advancements in rehabilitation sciences have progressively used computational techniques to improve diagnostic and treatment approaches. However, the analysis of high-dimensional, time-dependent data continues to pose a significant...