AIMC Topic: Cartilage, Articular
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Same-model and cross-model variability in knee cartilage thickness measurements using 3D MRI systems.
PloS one
Jun 13, 2025
PURPOSE: Magnetic Resonance Imaging (MRI) based three-dimensional analysis of knee cartilage has evolved to become fully automatic. However, when implementing these measurements across multiple clinical centers, scanner variability becomes a critical...
Contribution of Labrum and Cartilage to Joint Surface in Different Hip Deformities: An Automatic Deep Learning-Based 3-Dimensional Magnetic Resonance Imaging Analysis.
The American journal of sports medicine
Jun 7, 2025
BACKGROUND: Multiple 2-dimensional magnetic resonance imaging (MRI) studies have indicated that the size of the labrum adjusts in response to altered joint loading. In patients with hip dysplasia, it tends to increase as a compensatory mechanism for ...
Automated Detection of Microcracks Within Second Harmonic Generation Images of Cartilage Using Deep Learning.
Journal of orthopaedic research : official publication of the Orthopaedic Research Society
Mar 20, 2025
Articular cartilage, essential for smooth joint movement, can sustain micrometer-scale microcracks in its collagen network from low-energy impacts previously considered non-injurious. These microcracks may propagate under cyclic loading, impairing ca...
A deep learning approach for automatic 3D segmentation of hip cartilage and labrum from direct hip MR arthrography.
Scientific reports
Feb 7, 2025
The objective was to use convolutional neural networks (CNNs) for automatic segmentation of hip cartilage and labrum based on 3D MRI. In this retrospective single-center study, CNNs with a U-Net architecture were used to develop a fully automated seg...
Visualizing Preosteoarthritis: Updates on UTE-Based Compositional MRI and Deep Learning Algorithms.
Journal of magnetic resonance imaging : JMRI
Jan 10, 2025
Osteoarthritis (OA) is heterogeneous and involves structural changes in the whole joint, such as cartilage, meniscus/labrum, ligaments, and tendons, mainly with short T2 relaxation times. Detecting OA before the onset of irreversible changes is cruci...
Cell quantification at the osteochondral interface from synchrotron radiation phase contrast micro-computed tomography images using a deep learning approach.
Scientific reports
Nov 28, 2024
Osteochondral interface consists of two tissues: the calcified cartilage (CC) containing chondrocytes, and subchondral bone (SCB) containing osteocytes that interact with each other. In this study, we propose a new method for the three-dimensional (3...
Machine Learning Approaches for the Fusion of Near-Infrared, Mid-Infrared, and Raman Data to Identify Cartilage Degradation in Human Osteochondral Plugs.
Applied spectroscopy
Nov 8, 2024
Vibrational spectroscopy methods such as mid-infrared (MIR), near-infrared (NIR), and Raman spectroscopies have been shown to have great potential for in vivo biomedical applications, such as arthroscopic evaluation of joint injuries and degeneration...
Optimizing knee osteoarthritis severity prediction on MRI images using deep stacking ensemble technique.
Scientific reports
Nov 5, 2024
Knee osteoarthritis (KOA) represents a well-documented degenerative arthropathy prevalent among the elderly population. KOA is a persistent condition, also referred to as progressive joint Disease, stemming from the continual deterioration of cartila...
Deep Learning Reconstruction of Accelerated MRI: False-Positive Cartilage Delamination Inserted in MRI Arthrography Under Traction.
Topics in magnetic resonance imaging : TMRI
Jul 12, 2024
OBJECTIVES: The radiological imaging industry is developing and starting to offer a range of novel artificial intelligence software solutions for clinical radiology. Deep learning reconstruction of magnetic resonance imaging data seems to allow for t...
Classification of osteoarthritic and healthy cartilage using deep learning with Raman spectra.
Scientific reports
Jul 10, 2024
Raman spectroscopy is a rapid method for analysing the molecular composition of biological material. However, noise contamination in the spectral data necessitates careful pre-processing prior to analysis. Here we propose an end-to-end Convolutional ...
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