Computational modeling of revision total hip arthroplasty involving acetabular defects: A systematic review

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.