The menisci are 2 fibrocartilaginous crescents anchored via bony and ligamentous attachments to surrounding structures. Their biochemical composition and multilayered structure make them ideal for converting compressive forces to tensile forces in addition to improving joint congruity and providing shock absorption to weight bearing. The medial meniscus maintains more attachments at both the horns and the midbody than the lateral meniscus, making it more susceptible to injury. Understanding of the gross anatomy, vascular anatomy, biochemical composition, and microstructure is key to understanding causes of meniscal pathology as well as treatment options for restoring its primary functions.Copyright © 2019 Elsevier Inc. All rights reserved.

Over the past 2 decades there has been a profound shift in our perception of the role of the meniscus in the knee joint. Orthopaedic opinion now favors salvaging and restoring the damaged meniscus where possible. Basic science is characterizing its form (anatomy) and functionality (biological and biomechanical) in an attempt to understand the effect of meniscal injury and repair on the knee joint as a whole. The meniscus is a complex tissue and has warranted extensive basic science, translational, and clinical research to identify techniques to augment healing and even replace the meniscus. The application of quantitative magnetic resonance image sequencing to the meniscus and articular cartilage of the affected compartment promises to add a quantifiable outcome measure to the body of clinical evidence that supports restoration of the meniscus. This article discusses the recent advances and outcomes in the pursuit of meniscal restoration with particular focus on the use of augmentation strategies in meniscal repair, meniscal imaging, and translational strategies.

Knee osteoarthritis is a common degenerative joint disease characterized by chronic knee pain and disability in daily living. The lesion can involve the cartilage as well as the synovium, bone, ligaments, and meniscus, indicating a complicated pathology for knee osteoarthritis. The association with the meniscus has recently attracted much attention. Meniscal tears can initiate and progress knee osteoarthritis, with deleterious effects on the important roles of the meniscus in load distribution, shock absorption, and stability of the knee joint. Degenerative meniscus lesions are commonly observed in elderly people, but they have less impact on the prognosis of osteoarthritis. However, they are often accompanied by meniscal extrusion, which substantially decreases the hoop function of the meniscus and increases the risk of knee osteoarthritis. When surgical treatment is necessary, meniscal tears should be repaired to the greatest extent possible to preserve meniscus function. Long-term studies show better clinical outcomes and less degenerative osteoarthritis changes following meniscal repair than following partial meniscectomy. For meniscal extrusion repair, centralization techniques have been proposed that involve suturing the meniscus-capsule complex to the edge of the tibial plateau. Advancements in orthobiologics, such as platelet-rich plasma or stem cell therapy, have the potential to prevent the initiation or progression of osteoarthritis.

Precision medicine approaches rely on obtaining precise knowledge of the true state of health of an individual patient, which results from a combination of their genetic risks and environmental exposures. This approach is currently limited by the lack of effective and efficient non-invasive medical tests to define the full range of phenotypic variation associated with individual health. Such knowledge is critical for improved early intervention, for better treatment decisions, and for ameliorating the steadily worsening epidemic of chronic disease. We present proof-of-concept experiments to demonstrate how routinely acquired cross-sectional CT imaging may be used to predict patient longevity as a proxy for overall individual health and disease status using computer image analysis techniques. Despite the limitations of a modest dataset and the use of off-the-shelf machine learning methods, our results are comparable to previous ‘manual’ clinical methods for longevity prediction. This work demonstrates that radiomics techniques can be used to extract biomarkers relevant to one of the most widely used outcomes in epidemiological and clinical research – mortality, and that deep learning with convolutional neural networks can be usefully applied to radiomics research. Computer image analysis applied to routinely collected medical images offers substantial potential to enhance precision medicine initiatives.

Magnetic resonance (MR) imaging is currently the modality of choice for detecting meniscal injuries and planning subsequent treatment. A thorough understanding of the imaging protocols, normal meniscal anatomy, surrounding anatomic structures, and anatomic variants and pitfalls is critical to ensure diagnostic accuracy and prevent unnecessary surgery. High-spatial-resolution imaging of the meniscus can be performed using fast spin-echo and three-dimensional MR imaging sequences. Normal anatomic structures that can mimic a tear include the meniscal ligament, meniscofemoral ligaments, popliteomeniscal fascicles, and meniscomeniscal ligament. Anatomic variants and pitfalls that can mimic a tear include discoid meniscus, meniscal flounce, a meniscal ossicle, and chondrocalcinosis. When a meniscal tear is identified, accurate description and classification of the tear pattern can guide the referring clinician in patient education and surgical planning. For example, longitudinal tears are often amenable to repair, whereas horizontal and radial tears may require partial meniscectomy. Tear patterns include horizontal, longitudinal, radial, root, complex, displaced, and bucket-handle tears. Occasionally, meniscal tears can be difficult to detect at imaging; however, secondary indirect signs, such as a parameniscal cyst, meniscal extrusion, or linear subchondral bone marrow edema, should increase the radiologist's suspicion for an underlying tear. Awareness of common diagnostic errors can ensure accurate diagnosis of meniscal tears. Online supplemental material is available for this article.©RSNA, 2014.

To clinically validate a fully automated deep convolutional neural network (DCNN) for detection of surgically proven meniscus tears.One hundred consecutive patients were retrospectively included, who underwent knee MRI and knee arthroscopy in our institution. All MRI were evaluated for medial and lateral meniscus tears by two musculoskeletal radiologists independently and by DCNN. Included patients were not part of the training set of the DCNN. Surgical reports served as the standard of reference. Statistics included sensitivity, specificity, accuracy, ROC curve analysis, and kappa statistics.Fifty-seven percent (57/100) of patients had a tear of the medial and 24% (24/100) of the lateral meniscus, including 12% (12/100) with a tear of both menisci. For medial meniscus tear detection, sensitivity, specificity, and accuracy were for reader 1: 93%, 91%, and 92%, for reader 2: 96%, 86%, and 92%, and for the DCNN: 84%, 88%, and 86%. For lateral meniscus tear detection, sensitivity, specificity, and accuracy were for reader 1: 71%, 95%, and 89%, for reader 2: 67%, 99%, and 91%, and for the DCNN: 58%, 92%, and 84%. Sensitivity for medial meniscus tears was significantly different between reader 2 and the DCNN (p = 0.039), and no significant differences existed for all other comparisons (all p ≥ 0.092). The AUC-ROC of the DCNN was 0.882, 0.781, and 0.961 for detection of medial, lateral, and overall meniscus tear. Inter-reader agreement was very good for the medial (kappa = 0.876) and good for the lateral meniscus (kappa = 0.741).DCNN-based meniscus tear detection can be performed in a fully automated manner with a similar specificity but a lower sensitivity in comparison with musculoskeletal radiologists.

Objective: The objective of this study is to develop a novel automatic convolutional neural network (CNN) that aids in the diagnosis of meniscus injury, while enabling the visualization of lesion characteristics. This will improve the accuracy and reduce diagnosis times. Methods: We presented a cascaded-progressive convolutional neural network (C-PCNN) method for diagnosing meniscus injuries using magnetic resonance imaging (MRI). A total of 1396 images collected in the hospital were used for training and testing. The method used for training and testing was 5-fold cross validation. Using intraoperative arthroscopic diagnosis and MRI diagnosis as criteria, the C-PCNN was evaluated based on accuracy, sensitivity, specificity, receiver operating characteristic (ROC), and evaluation performance. At the same time, the diagnostic accuracy of doctors with the assistance of cascade- progressive convolutional neural networks was evaluated. The diagnostic accuracy of a C-PCNN assistant with an attending doctor and chief doctor was compared to evaluate the clinical significance. Results: C-PCNN showed 85.6% accuracy in diagnosing and identifying anterior horn injury, and 92% accuracy in diagnosing and identifying posterior horn injury. The average accuracy of C-PCNN was 89.8%, AUC = 0.86. The diagnosis accuracy of the attending physician with the aid of the C-PCNN was comparable to that of the chief physician. Conclusion: The C-PCNN-based MRI technique for diagnosing knee meniscus injuries has significant practical value in clinical practice. With a high rate of accuracy, clinical auxiliary physicians can increase the speed and accuracy of diagnosis and decrease the number of incorrect diagnoses.

The aim of this study was to discuss the treatment of extra-articular distal humeral shaft fractures using ultrasound-guided preoperative localization of radial nerve.