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Revision total hip arthroplasty (rTHA) involving acetabular defects is a complex procedure associated with lower rates of success than primary THA. Computational modeling has played a key role in surgical planning and prediction of postoperative outcomes following primary THA, but modeling applications in rTHA for acetabular defects remain poorly understood. This study aimed to systematically review the use of computational modeling in acetabular defect classification, implant selection and placement, implant design, and postoperative joint functional performance evaluation following rTHA involving acetabular defects. The databases of Web of Science, Scopus, Medline, Embase, Global Health and Central were searched. Fifty‐three relevant articles met the inclusion criteria, and their quality were evaluated using a modified Downs and Black evaluation criteria framework. Manual image segmentation from computed tomography scans, which is time consuming, remains the primary method used to generate 3D models of hip bone; however, statistical shape models, once developed, can be used to estimate pre‐defect anatomy rapidly. Finite element modeling, which has been used to estimate bone stresses and strains, and implant micromotion postoperatively, has played a key role in custom and off‐the‐shelf implant design, mitigation of stress shielding, and prediction of bone remodeling and implant stability. However, model validation is challenging and requires rigorous evaluation and comparison with respect to mid‐ to long‐term clinical outcomes. Development of fast, accurate methods to model acetabular defects, including statistical shape models and artificial neural networks, may ultimately improve uptake of and expand applications in modeling and simulation of rTHA for the research setting and clinic.
Revision total hip arthroplasty (rTHA) involving acetabular defects is a complex procedure associated with lower rates of success than primary THA. Computational modeling has played a key role in surgical planning and prediction of postoperative outcomes following primary THA, but modeling applications in rTHA for acetabular defects remain poorly understood. This study aimed to systematically review the use of computational modeling in acetabular defect classification, implant selection and placement, implant design, and postoperative joint functional performance evaluation following rTHA involving acetabular defects. The databases of Web of Science, Scopus, Medline, Embase, Global Health and Central were searched. Fifty‐three relevant articles met the inclusion criteria, and their quality were evaluated using a modified Downs and Black evaluation criteria framework. Manual image segmentation from computed tomography scans, which is time consuming, remains the primary method used to generate 3D models of hip bone; however, statistical shape models, once developed, can be used to estimate pre‐defect anatomy rapidly. Finite element modeling, which has been used to estimate bone stresses and strains, and implant micromotion postoperatively, has played a key role in custom and off‐the‐shelf implant design, mitigation of stress shielding, and prediction of bone remodeling and implant stability. However, model validation is challenging and requires rigorous evaluation and comparison with respect to mid‐ to long‐term clinical outcomes. Development of fast, accurate methods to model acetabular defects, including statistical shape models and artificial neural networks, may ultimately improve uptake of and expand applications in modeling and simulation of rTHA for the research setting and clinic.
To analyse the strength and mechanical behaviour of hip implants, it is essential to employ an appropriate loading model. Generating computational models supplemented with muscle forces is a complicated task, especially in the initial phase of implant development. This research aims to expand the possibilities of the simpler acetabular cage model based on joint loads without significantly increasing the demand for computing resources. A Python script covered and grouped the loads from daily activities. The ten calculated major loads were compared with the maximum of the walking and stair climbing loads through the finite element analyses of a custom-made acetabular cage. Sensitivity analyses were performed for the surrounding bones’ elastic modulus and the pelvis boundary conditions. The major loads can geometrically cover the entire load spectrum of daily activities. The effect of many high-magnitude force vectors is uncertain in the approach that uses the most common maximum loads. Using these resultant major loads, a new stress concentration area could be detected on the acetabular cage, besides the stress concentration areas induced by the loads reported in the literature. The qualitative correctness of the results is also supported by a control computed tomography scan: a fracture occurred in an extensive, high-stress zone. The results are not sensitive to changes in the elastic modulus of the surrounding bone and the boundary conditions of the model. The presented load vectors and the algorithm make more extensive static analyses possible with little computational overhead. The proposed method can be used for checking the static strength of similar implants.
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