Objective: Although it is established that structural damage of the meniscus is linked to knee osteoarthritis (OA) progression, the predisposition to future development of OA because of geometric meniscal shapes is plausible and unexplored. This study aims to identify common variations in meniscal shape and determine their relationships to tissue morphology, OA onset, and longitudinal changes in cartilage thickness.
Methods: A total of 4,790 participants from the Osteoarthritis Initiative data set were studied. A statistical shape model was developed for the meniscus, and shape scores were evaluated between a control group and an OA incidence group. Shape features were then associated with cartilage thickness changes over 8 years to localize the relationship between meniscus shape and cartilage degeneration.
Results: Seven shape features between the medial and lateral menisci were identified to be different between knees that remain normal and those that develop OA. These include length-width ratios, horn lengths, root attachment angles, and concavity. These "at-risk" shapes were linked to unique cartilage thickness changes that suggest a relationship between meniscus geometry and decreased tibial coverage and rotational imbalances. Additionally, strong associations were found between meniscal shape and demographic subpopulations, future tibial extrusion, and meniscal and ligamentous tears.
Conclusion: This automatic method expanded upon known meniscus characteristics that are associated with the onset of OA and discovered novel shape features that have yet to be investigated in the context of OA risk.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10706605 | PMC |
http://dx.doi.org/10.1002/art.42623 | DOI Listing |
Plants (Basel)
January 2025
Department of Horticulture, National Chung Hsing University, Taichung City 40227, Taiwan.
Trees are complex and dynamic living structures, where structural stability is essential for survival and for public safety in urban environments. Tree forks, as structural junctions, are key to tree integrity but are prone to failure under stress. The specific mechanical contributions of their internal conical structures remain largely unexplored.
View Article and Find Full Text PDFSensors (Basel)
January 2025
School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China.
With the rapid development of AI algorithms and computational power, object recognition based on deep learning frameworks has become a major research direction in computer vision. UAVs equipped with object detection systems are increasingly used in fields like smart transportation, disaster warning, and emergency rescue. However, due to factors such as the environment, lighting, altitude, and angle, UAV images face challenges like small object sizes, high object density, and significant background interference, making object detection tasks difficult.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Airworthiness Division, Air Force Institute of Technology, 01-494 Warsaw, Poland.
The range of sensor technologies for structural health monitoring (SHM) systems is expanding as the need for ongoing structural monitoring increases. In such a case, damage to the monitored structure elements is detected using an integrated network of sensors operating in real-time or periodically in frequent time stamps. This paper briefly introduces a new type of sensor, called a Customized Crack Propagation Sensor (CCPS), which is an alternative for crack gauges, but with enhanced functional features and customizability.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Institute for Energy Engineering, Universitat Politècnica de València, Camino. de Vera s/n, 46022 Valencia, Spain.
Induction motors are essential components in industry due to their efficiency and cost-effectiveness. This study presents an innovative methodology for automatic fault detection by analyzing images generated from the Fourier spectra of current signals using deep learning techniques. A new preprocessing technique incorporating a distinctive background to enhance spectral feature learning is proposed, enabling the detection of four types of faults: healthy motor coupled to a generator with a broken bar (HGB), broken rotor bar (BRB), race bearing fault (RBF), and bearing ball fault (BBF).
View Article and Find Full Text PDFSensors (Basel)
January 2025
College of Metrology Measurement and Instrument, China Jiliang University, Hangzhou 310018, China.
This paper aims to address the challenge of precise robotic grasping of molecular sieve drying bags during automated packaging by proposing a six-dimensional (6D) pose estimation method based on an red green blue-depth (RGB-D) camera. The method consists of three components: point cloud pre-segmentation, target extraction, and pose estimation. A minimum bounding box-based pre-segmentation method was designed to minimize the impact of packaging wrinkles and skirt curling.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!