RESUMEN
Osteoarthritis (OA) is a common degenerative joint disease that affects millions of people worldwide. Magnetic resonance imaging (MRI) has emerged as a powerful tool for the evaluation and monitoring of OA due to its ability to visualize soft tissues and bone with high resolution. This review aims to provide an overview of the current state of MRI in OA, with a special focus on the knee, including protocol recommendations for clinical and research settings. Furthermore, new developments in the field of musculoskeletal MRI are highlighted in this review. These include compositional MRI techniques, such as T2 mapping and T1rho imaging, which can provide additional important information about the biochemical composition of cartilage and other joint tissues. In addition, this review discusses semiquantitative joint assessment based on MRI findings, which is a widely used method for evaluating OA severity and progression in the knee. We analyze the most common scoring methods and discuss potential benefits. Techniques to reduce acquisition times and the potential impact of deep learning in MR imaging for OA are also discussed, as these technological advances may impact clinical routine in the future.
RESUMEN
INTRODUCTION: Robotic-assisted surgery techniques are increasing in total knee arthroplasty (TKA). One crucial point is the prolonged time of surgery. The primary objective of this study was to determine the learning curve necessary to minimize the time of surgery. The secondary objective was to evaluate the accuracy of the implant alignment when using an imageless robotic system for TKA. MATERIALS AND METHODS: In a case-control study, the first 70 consecutive robotic-assisted TKA procedures performed by a single senior surgeon were analyzed with regard to surgery time and implant alignment by comparing the intraoperative plan with the postoperative alignment. The evaluation of the learning curve with respect to surgery time was conducted using cumulative summation (CUSUM) analysis. The joint line height was measured with a new technique. Surgery time and joint line reconstruction were compared to 70 consecutive conventional TKA procedures. RESULTS: The learning curve for robotic TKA was completed after 11 cases. The learning curve did not influence the accuracy of joint line obliquity, joint line height, or limb alignment. The intraoperative plan designed for the robotic system was precisely implemented. The mean skin-to-skin time in the robotic group after the learning curve was completed did not differ from that in the manual group. A significant positive correlation was observed between the preoperative hip-knee-ankle angle and the postoperative distalization of the joint line in the robotic-assisted TKA group. CONCLUSION: After completing the initial learning curve of 11 cases, the surgery time required to perform imageless robotic handpiece-assisted TKA was similar to that for the conventional technique. However, no learning curve was observed for the implant positioning when using the imageless robotic system. The implementation of the intraoperative plan was accurate up to < 2°. The precision of the system allows the implementation of different joint balancing approaches between valgus and varus morphotypes.
