A Comprehensive Framework to Evaluate the Effects of Anterior Cruciate Ligament Injury and Reconstruction on Graft and Cartilage Status through the Analysis of MRI T2 Relaxation Time and Knee Laxity: A Pilot Study

Multiscale correlations between joint and tissue-specific biomechanics and anatomy in postmortem ovine stifles

Joint stability depends on various properties that include the bone anatomy, joint capsule geometry and stiffness, and soft tissues within. The multiscale biomechanical relationships between the whole joint and individual tissues are useful in estimating the physiological condition of the knee. To better understand these relationships, we evaluated multiple structural and mechanical parameters in healthy ovine stifles, specifically joint laxity, joint morphology, individual tissue T2* relaxation and mechanical properties of ligaments (ACL, PCL, MCL, LCL), patellar tendon, menisci, and cartilage. By combining mechanical testing at two length scales and magnetic resonance imaging (MRI) scans, we quantify the strength of correlation coefficients between measured metrics, among joint size, whole joint and individual tissue properties. We observe positive correlations between joint laxity forces and the inter-epicondylar distance. Further, the viscoelastic properties of the tendons and ligaments correlate positively with joint laxity forces; however, no such correlations were observed for the cartilage and menisci. We also found weak inverse correlation between tissue viscoelasticity and T2* for MCL; and positive correlations for cartilage and menisci, LCL and PCL respectively. These results provide useful insights into the role of individual tissues that are crucial in measuring whole joint responses as key indicators of knee health.

Refined Detection and Classification of Knee Ligament Injury Based on ResNet Convolutional Neural Networks

Currently, medical imaging has largely supplanted traditional methods in the realm of diagnosis and treatment planning. This shift is primarily attributable to the non-invasive nature, rapidity, and user-friendliness of medical-imaging techniques. The widespread adoption of medical imaging, however, has shifted the bottleneck to healthcare professionals who must analyze each case post-image acquisition. This process is characterized by its sluggishness and subjectivity, making it susceptible to errors. The anterior cruciate ligament (ACL), a frequently injured knee ligament, predominantly affects a youthful and sports-active demographic. ACL injuries often leave patients with substantial disabilities and alter knee mechanics. Since some of these cases necessitate surgery, it is crucial to accurately classify and detect ACL injury. This paper investigates the utilization of pre-trained convolutional neural networks featuring residual connections (ResNet) along with image-processing methods to identify ACL injury and differentiate between various tear levels. The ResNet employed in this study is not the standard ResNet but rather an adapted version capable of processing 3D volumes constructed from 2D image slices. Achieving a peak accuracy of 97.15% with a custom split, 96.32% through Monte-Carlo cross-validation, and 93.22% via five-fold cross-validation, our approach enhances the performance of three-class classifiers by over 7% in terms of raw accuracy. Moreover, we achieved an improvement of more than 1% across all types of evaluation. It is quite clear that the model's output can effectively serve as an initial diagnostic baseline for radiologists with minimal effort and nearly instantaneous results. This advancement underscores the paper's focus on harnessing deep learning for the nuanced detection and classification of ACL tears, demonstrating a significant leap toward automating and refining diagnostic accuracy in sports medicine and orthopedics.

Advanced MR Imaging for Knee Osteoarthritis: A Review on Local and Brain Effects

Knee osteoarthritis is one of the leading causes of chronic disability worldwide and is a significant social and economic burden on healthcare systems; hence it has become essential to develop methods to identify patients at risk for developing knee osteoarthritis at an early stage. Standard morphological MRI sequences are focused mostly on alterations seen in advanced stages of osteoarthritis. However, they possess low sensitivity for early, subtle, and potentially reversible changes of the degenerative process. In this review, we have summarized the state of the art with regard to innovative quantitative MRI techniques that exploit objective and quantifiable biomarkers to identify subtle alterations that occur in early stages of osteoarthritis in knee cartilage before any morphological alteration occurs and to capture potential effects on the brain. These novel MRI imaging tools are believed to have great potential for improving the current standard of care, but further research is needed to address limitations before these compositional techniques can be robustly applied in research and clinical settings.