Achieving accuracy and gap balancing with fully autonomous Cuvis Joint Robot assisted total knee arthroplasty: A single-center, non-randomized retrospective study - Shailendra Patil; Chetan Wankhede (Journal of Orthopaedic Reports)
Background: Achieving accuracy and precision during total knee arthroplasty (TKA) is challenging due to complex knee anatomy, patient demographic and expectations of younger patients opting for the surgery. Real-time input and personalization help surgeons achieve their aim with robotic surgery. Method: This single-center, non-randomized, retrospective study included male and female patients over 18 years with primary OA knee with Varus and Valgus deformity, and able to provide written informed consent. Patients with infected TKR requiring revisional surgery and those reluctant to engage in the trial were excluded. The accuracy and efficiency of the fully automated Joint Robot was measured by the difference between planned and measured resection thickness and angle. Results: The study enrolled 100 patients of whom 53 % underwent RA-TKA of right knee and 47 % had left knee RA-TKA. Women patients dominated the study (69 %), and overall average age of the patients enrolled was 65.55 ± 9.15 years. The difference in mean resection thickness was well within the desired limit, with root mean square of 0.78 mm and 0.96 mm in medial distal and lateral distal planes; 0.94 mm and 1.13 mm in posterior femoral planes. Medial tibial as well as lateral tibial planes were 0.83 and 0.95 mm. The differences in mean resection angle were 0.77° (varus/valgus [V/V]), 0.98° (flexion/extension [F/E]) at distal femoral plane and 0.59° (V/V) and 1.5° (F/E) at posterior femoral plane. At tibial plane V/V was 0.76° and 0.85 °F/E. Conclusion: The study's results support the efficacy and precision of RA-TKA, showcasing its ability to maintain resection thickness and angles in line with anticipated values. These findings solidify the reliability and safety of robotic assisted total knee arthroplasty, characterized by minimal variations in resection parameters.