BackgroundComputer-assisted total knee replacement (TKR) has been shown to improve radiographic alignment and therefore the clinical outcome. Outliers with greater than 3° of varus or valgus malalignment in TKR can suffer higher failure rates. The aim of this study was to determine the impact of experience with both computer navigation and knee replacement surgery on the frequency of errors in intraoperative bone cuts and implant alignment, as well as the actual learning curve.Materials and methodsThree homogeneous groups who underwent computer-assisted TKR were included in the study: group A [surgery performed by a surgeon experienced in both TKR and computer-assisted surgery (CAS)], B [surgery performed by a surgeon experienced in TKR but not CAS], and C [surgery performed by a general orthopedic surgeon]. In other words, all of the surgeons had different levels of experience in TKR and CAS, and each group was treated by only one of the surgeons. Cutting errors, number of re-cuts, complications, and mean surgical times were recorded. Frontal femoral component angle, frontal tibial component angle, hip–knee–ankle angle, and component slopes were evaluated.ResultsThe number of cutting errors varied significantly: the lowest number was recorded for TKR performed by the surgeon with experience in CAS. Superior results were achieved in relation to final mechanical axis alignment by the surgeon experienced in CAS compared to the other surgeons. However, the total number of outliers showed no statistically significant difference among the three surgeons. After 11 cases, there were no differences in the number of re-cuts between groups A and C, and after 9 cases there were no differences in surgical time between groups A and B.ConclusionA beginner can reproduce the results of an expert TKR surgeon by means of navigation (i.e., CAS) after a learning curve of 16 cases; this represents the break-even point after which no statistically significant difference is observed between the expert surgeon and the beginner utilizing CAS.

Fracture of the metallic components is a potential cause of failure of unicompartmental knee arthroplasty. In our experience, the incidence of this complication was 4.9 % of all UKR failures. Patients with a BMI greater than 30 and a progressive deterioration in limb alignment were at greater risk.

We believe that the MIPO technique for distal fractures of the fibula should be used more often, especially if soft tissue is in a critical condition. Healing times should be reduced in the more complex cases. It is important that the learning curve should be improved, to minimize exposure to radioscopy and possible damage to the superficial fibular nerve.

2D- and 3D-based innovative methods for surgical planning and simulation systems in orthopedic surgery have emerged enabling the interactive or semi-automatic identification of the clinical landmarks (CL) on the patient individual virtual bone anatomy. They enable the determination of the optimal implant sizes and positioning according to the computed CL, the visualization of the virtual bone resections and the simulation of the overall intervention prior to surgery. The virtual palpation of CL, highly dependent upon the examiner's expertise, was proved to be time consuming and to suffer from considerable inter-observer variability. In this article, we propose a fully automatic algorithmic framework that processes the pelvic bone surface, integrating surface curvature analysis, quadric fitting, recursive clustering and clinical knowledge, aiming at computing the main parameters of the acetabulum. The performance of the method was evaluated using pelvic bone surfaces reconstructed from CT scans of cadavers and subjects with pathological conditions at the hip joint. The repeatability error of the automated computation of acetabular center, size and axis parameters was less than 1 mm, 0.5 mm, and 1.5°, respectively. The computed parameters were in agreement (<1.5 mm; <0.5 mm; <3.0°) with the corresponding reference parameters manually identified in the original datasets by medical experts. According to our results, the proposed method is put forward to improve the degree of automation of image/model-based planning systems for hip surgery.

This study presents a consecutive series of patients who underwent total knee arthroplasty (TKA) after prior distal femoral fracture without hardware removal. The purpose of this study was to determine the effectiveness of computer-assisted TKA in patients with posttraumatic arthritis, specifically those with retained hardware after prior distal femoral fracture. The study group included a consecutive series of 16 patients who had developed posttraumatic knee arthritis after a distal femoral fracture with retention of hardware (group A). Patients in the study group were matched with patients who had undergone a computer-assisted TKA using the same implant and software (group B). The indication for TKA in all group B patients was atraumatic arthritis, and surgery was performed during the same period as that in the study group. Patients were matched for age, sex, preoperative range of motion, preoperative severity of arthritis, type and grade of deformity, and implant features. No statistically significant differences existed between the 2 study groups in terms of operative time, duration of hospital stay, or intra- and postoperative complications. At last follow-up, no statistically significant differences existed in Knee Society Scores and Western Ontario and McMaster Universities Arthritis Index scores. Implant alignment and radiological parameters were similar in both groups. This study demonstrated that posttraumatic knee arthritis after prior distal femoral fracture can be safely managed using a computer-assisted TKA without hardware removal. Comparison between the study group and a matched group with atraumatic arthritis showed similar postoperative results and complication rates.