Robot-assisted unicompartmental knee surgery has been shown to improve the accuracy of implant alignment. However, little research has been conducted to ascertain if this results in a measureable improvement in knee function post operatively and a more normal gait. The kinematics of 70 OA knees were assessed using motion analysis in an RCT (31 receiving robotic-assisted surgery, and 39 receiving traditional manual surgery) and compared to healthy knees. Statistically significant kinematic differences were seen between the two surgical groups from foot-strike to mid-stance. The robotic-assisted group achieved a higher knee excursion (18.0°, SD 4.9°) compared to the manual group (15.7°, SD 4.1°). There were no significant difference between the healthy group and the robotic assisted group, however there was a significant difference between the healthy group and the manual group (p < 0.001). Hence robotically-assisted knee replacement with Mako Restoris Implants appears to lead not only to better implant alignment but also some kinematic benefits to the user during gait.

Aims No predictive model has been published to forecast operating time for total knee arthroplasty (TKA). The aims of this study were to design and validate a predictive model to estimate operating time for robotic-assisted TKA based on demographic data, and evaluate the added predictive power of CT scan-based predictors and their impact on the accuracy of the predictive model. Methods A retrospective study was conducted on 1,061 TKAs performed from January 2016 to December 2019 with an image-based robotic-assisted system. Demographic data included age, sex, height, and weight. The femoral and tibial mechanical axis and the osteophyte volume were calculated from CT scans. These inputs were used to develop a predictive model aimed to predict operating time based on demographic data only, and demographic and 3D patient anatomy data. Results The key factors for predicting operating time were the surgeon and patient weight, followed by 12 anatomical parameters derived from CT scans. The predictive model based only on demographic data showed that 90% of predictions were within 15 minutes of actual operating time, with 73% within ten minutes. The predictive model including demographic data and CT scans showed that 94% of predictions were within 15 minutes of actual operating time and 88% within ten minutes. Conclusion The primary factors for predicting robotic-assisted TKA operating time were surgeon, patient weight, and osteophyte volume. This study demonstrates that incorporating 3D patient-specific data can improve operating time predictions models, which may lead to improved operating room planning and efficiency. Cite this article: Bone Jt Open 2022;3(5):383–389.

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