AIMC Topic: Muscle, Skeletal
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Design and Validation of a Pancake Style Planetary Gearbox for an Eddy Current-Based Wearable Gait Training Robot.
IEEE transactions on bio-medical engineering
Jan 15, 2025
Eddy current brakes have been recently used for functional resistance training in individuals with neurological and orthopaedic disorders. These devices consist of a gearbox, a conductive disc, and permanent magnets that can be moved relative to the ...
Importance of neural network complexity for the automatic segmentation of individual thigh muscles in MRI images from patients with neuromuscular diseases.
Magma (New York, N.Y.)
Jan 11, 2025
OBJECTIVE: Segmentation of individual thigh muscles in MRI images is essential for monitoring neuromuscular diseases and quantifying relevant biomarkers such as fat fraction (FF). Deep learning approaches such as U-Net have demonstrated effectiveness...
Aged
Adult
Male
Middle Aged
Magnetic Resonance Imaging
Muscle, Skeletal
Adipose Tissue
Deep Learning
Image Processing, Computer-Assisted
Algorithms
Databases, Factual
Female
Thigh
Reproducibility of Results
Image Interpretation, Computer-Assisted
Neuromuscular Diseases
Humans
Neural Networks, Computer
Artificial intelligence for body composition assessment focusing on sarcopenia.
Scientific reports
Jan 8, 2025
This study aimed to address the limitations of conventional methods for measuring skeletal muscle mass for sarcopenia diagnosis by introducing an artificial intelligence (AI) system for direct computed tomography (CT) analysis. The primary focus was ...
Multilabel segmentation and analysis of skeletal muscle and adipose tissue in routine abdominal CT scans.
Computers in biology and medicine
Jan 7, 2025
PURPOSE: This paper presents a deep learning-based multi-label segmentation network that extracts a total of three separate adipose tissues and five different muscle tissues in CT slices of the third lumbar vertebra and additionally improves the segm...
Validation of musculoskeletal segmentation model with uncertainty estimation for bone and muscle assessment in hip-to-knee clinical CT images.
Scientific reports
Jan 2, 2025
Deep learning-based image segmentation has allowed for the fully automated, accurate, and rapid analysis of musculoskeletal (MSK) structures from medical images. However, current approaches were either applied only to 2D cross-sectional images, addre...
Exploring determinant factors influencing muscle quality and sarcopenia in Bilbao's older adult population through machine learning: A comprehensive analysis approach.
PloS one
Dec 31, 2024
BACKGROUND: Sarcopenia and reduced muscle quality index have garnered special attention due to their prevalence among older individuals and the adverse effects they generate. Early detection of these geriatric pathologies holds significant potential,...
Correlation between individual thigh muscle volume and grip strength in relation to sarcopenia with automated muscle segmentation.
PloS one
Dec 26, 2024
INTRODUCTION: Grip strength serves as a significant marker for diagnosing and assessing sarcopenia, particularly in elderly populations. The study aims to explore the relationship between individual thigh muscle volumes and grip strength, leveraging ...
Identifying threshold of CT-defined muscle loss after radiotherapy for survival in oral cavity cancer using machine learning.
European radiology
Dec 20, 2024
OBJECTIVES: Muscle loss after radiotherapy is associated with poorer survival in patients with oral cavity squamous cell carcinoma (OCSCC). However, the threshold of muscle loss remains unclear. This study aimed to utilize explainable artificial inte...
Augmented Effect of Combined Robotic Assisted Gait Training and Proprioceptive Neuromuscular Facilitation-irradiation Technique on Muscle Activation and Ankle Kinematics in Hemiparetic Gait: A Preliminary Study.
NeuroRehabilitation
Dec 19, 2024
BackgroundProprioceptive neuromuscular facilitation (PNF) alone has limited effectiveness in restoring gait, while robotic-assisted gait training (RAGT) improves motor relearning through repetitive, task-specific movements. Combining PNF with robotic...
A U-Net based partial convolutional time-domain separation model to identify motor units from surface electromyographic signals in real time.
Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology
Dec 19, 2024
This study proposed a U-Net based partial convolutional time-domain model for a real-time high-density surface electromyography (HD-sEMG) decomposition. The model combines U-Net and a separation block containing partial convolution, aiming to efficie...
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