Aims. Accurate identification of the ankle joint centre is critical for estimating tibial coronal alignment in total knee arthroplasty (TKA). The purpose of the current study was to leverage artificial intelligence (AI) to determine the accuracy and effect of using different radiological anatomical landmarks to quantify mechanical alignment in relation to a traditionally defined radiological ankle centre. Methods. Patients with full-limb radiographs from the Osteoarthritis Initiative were included. A sub-cohort of 250 radiographs were annotated for landmarks relevant to knee alignment and used to train a deep learning (U-Net)
Aims. Distal femoral resection in conventional total knee arthroplasty (TKA) utilizes an intramedullary guide to determine coronal alignment, commonly planned for 5° of valgus. However, a standard 5° resection angle may contribute to malalignment in patients with variability in the femoral anatomical and mechanical axis angle. The purpose of the study was to leverage deep learning (DL) to measure the femoral mechanical-anatomical axis angle (FMAA) in a heterogeneous cohort. Methods. Patients with full-limb radiographs from the Osteoarthritis Initiative were included. A DL
The surgical target for optimal implant positioning in robotic-assisted total knee arthroplasty remains the subject of ongoing discussion. One of the proposed targets is to recreate the knee’s functional behaviour as per its pre-diseased state. The aim of this study was to optimize implant positioning, starting from mechanical alignment (MA), toward restoring the pre-diseased status, including ligament strain and kinematic patterns, in a patient population. We used an active appearance model-based approach to segment the preoperative CT of 21 osteoarthritic patients, which identified the osteophyte-free surfaces and estimated cartilage from the segmented bones; these geometries were used to construct patient-specific musculoskeletal models of the pre-diseased knee. Subsequently, implantations were simulated using the MA method, and a previously developed optimization technique was employed to find the optimal implant position that minimized the root mean square deviation between pre-diseased and postoperative ligament strains and kinematics.Aims
Methods
The objective of this study is to assess the use of ultrasound (US) as a radiation-free imaging modality to reconstruct 3D anatomy of the knee for use in preoperative templating in knee arthroplasty. Using an US system, which is fitted with an electromagnetic (EM) tracker that is integrated into the US probe, allows 3D tracking of the probe, femur, and tibia. The raw US radiofrequency (RF) signals are acquired and, using real-time signal processing, bone boundaries are extracted. Bone boundaries and the tracking information are fused in a 3D point cloud for the femur and tibia. Using a statistical shaping model, the patient-specific surface is reconstructed by optimizing bone geometry to match the point clouds. An accuracy analysis was conducted for 17 cadavers by comparing the 3D US models with those created using CT. US scans from 15 users were compared in order to examine the effect of operator variability on the output.Aims
Methods
The mobile bearing Oxford unicompartmental knee arthroplasty (OUKA) is recommended to be performed with the leg in the hanging leg (HL) position, and the thigh placed in a stirrup. This comparative cadaveric study assesses implant positioning and intraoperative kinematics of OUKA implanted either in the HL position or in the supine leg (SL) position. A total of 16 fresh-frozen knees in eight human cadavers, without macroscopic anatomical defects, were selected. The knees from each cadaver were randomized to have the OUKA implanted in the HL or SL position.Aims
Methods
The use of the haptically bounded saw blades in robotic-assisted total knee arthroplasty (RTKA) can potentially help to limit surrounding soft-tissue injuries. However, there are limited data characterizing these injuries for cruciate-retaining (CR) TKA with the use of this technique. The objective of this cadaver study was to compare the extent of soft-tissue damage sustained through a robotic-assisted, haptically guided TKA (RATKA) A total of 12 fresh-frozen pelvis-to-toe cadaver specimens were included. Four surgeons each prepared three RATKA and three MTKA specimens for cruciate-retaining TKAs. A RATKA was performed on one knee and a MTKA on the other. Postoperatively, two additional surgeons assessed and graded damage to 14 key anatomical structures in a blinded manner. Kruskal–Wallis hypothesis tests were performed to assess statistical differences in soft-tissue damage between RATKA and MTKA cases.Objectives
Methods
Static radiostereometric analysis (RSA) using implanted markers is considered the most accurate system for the evaluation of prosthesis migration. By using CT bone models instead of markers, combined with a dynamic RSA system, a non-invasive measurement of joint movement is enabled. This method is more accurate than current 3D skin marker-based tracking systems. The purpose of this study was to evaluate the accuracy of the CT model method for measuring knee joint kinematics in static and dynamic RSA using the marker method as the benchmark. Bone models were created from CT scans, and tantalum beads were implanted into the tibia and femur of eight human cadaver knees. Each specimen was secured in a fixture, static and dynamic stereoradiographs were recorded, and the bone models and marker models were fitted to the stereoradiographs.Objectives
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