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Purpose: The aim of this study was to outline a fully automatic tool capable of reliably predicting the most suitable total knee replacement implant sizes for patients, using bi-planar X-ray images. By eliminating the need for manual templating or guiding software tools via the adoption of convolutional neural networks, time and resource requirements for pre-operative assessment and surgery could be reduced, the risk of human error minimized, and patients could see improved outcomes. Methods: The tool utilizes a machine learning-based 2D-3D pipeline to generate accurate predictions of subjects' distal femur and proximal tibia bones from X-ray images. It then virtually fits different implant models and sizes to the 3D predictions, calculates the implant to bone root-mean-squared error and maximum over/under hang for each, and advises the best option for the patient. The tool was tested on 78, predominantly White subjects (45 female/33 male), using generic femur component and tibia plate designs scaled to sizes obtained for five commercially available products. The predictions were then compared to the ground truth best options, determined using subjects' MRI data. Results: The tool achieved average femur component size prediction accuracies across the five implant models of 77.95% in terms of global fit (root-mean-squared error), and 71.79% for minimizing over/underhang. These increased to 99.74% and 99.49% with ±1 size permitted. For tibia plates, the average prediction accuracies were 80.51% and 72.82% respectively. These increased to 99.74% and 98.98% for ±1 size. Better prediction accuracies were obtained for implant models with fewer size options, however such models more frequently resulted in a poor fit. Conclusion: A fully automatic tool was developed and found to enable higher prediction accuracies than generally reported for manual templating techniques, as well as similar computational methods.
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INTRODUCTION: Acetabular component revision surgery can be a challenging task due to the encountered bone defects. Both cemented and uncemented techniques are described. We report on the survivorship of the Thackray cross plate with rim reinforcement ring for cemented acetabular revision. PATIENTS AND METHODS: This is a retrospective case series of all patients treated with the implant with a minimum follow-up of 2 years. Acetabular defects were characterized according to the Paprosky classification. Data on potential risk factors for failure of the construct as well as the Oxford Hip Score (OHS) were collected. Kaplan-Meier survival analysis with radiographic aseptic loosening or revision for aseptic loosening as the end point was performed. RESULTS: From 2000 to 2017, 35 revisions in 18 male and 17 female patients with an average age of 72 years were included. Bone allograft was used in 26 cases and additional implants (medial or supero-lateral mesh) in 13. Seven patients have deceased and the fate of all revisions is known. At an average clinical follow-up of 9.7 (2.6 to 19.6) years, there were no further re-revisions for construct failure. Five hips have demonstrated radiological evidence of aseptic loosening. Radiologically loose components were associated with more severe grades of acetabular bone defects (Paprosky Type 3) (60% vs 3%, p = 0.006). Kaplan-Meier survival analysis demonstrates 79.8% overall survivorship at 7 years. Survivorship for Type 2 defects was significantly higher compared to Type 3 (90% vs 0% at 7 years, Logrank test p = 0.002, Cox proportional hazards p = 0.03). The final median OHS was 38 (12-48) and was not affected by component loosening. CONCLUSION: This is a cost-effective device that protects the underlying bone graft (81% complete remodeling) and prevents subsidence of the cemented cup (2 mm on average). It should be used with caution in high-grade defects and perhaps not advised.