Daniel Hopkins, Stuart A. Callary, L. B. Solomon, Sarah C. Woodford, Peter V. S. Lee, David C. Ackland
{"title":"Computational modeling of revision total hip arthroplasty involving acetabular defects: A systematic review","authors":"Daniel Hopkins, Stuart A. Callary, L. B. Solomon, Sarah C. Woodford, Peter V. S. Lee, David C. Ackland","doi":"10.1002/jor.25902","DOIUrl":null,"url":null,"abstract":"<p>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.</p>","PeriodicalId":16650,"journal":{"name":"Journal of Orthopaedic Research®","volume":null,"pages":null},"PeriodicalIF":2.1000,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jor.25902","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Orthopaedic Research®","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/jor.25902","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ORTHOPEDICS","Score":null,"Total":0}
引用次数: 0
Abstract
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.
涉及髋臼缺损的翻修全髋关节置换术(rTHA)是一种复杂的手术,其成功率低于初次全髋关节置换术。计算建模在初次全髋关节置换术后的手术规划和术后效果预测中发挥了关键作用,但建模在髋臼缺损翻修全髋关节置换术中的应用仍鲜为人知。本研究旨在系统回顾计算建模在髋臼缺损 rTHA 术后髋臼缺损分类、植入物选择和放置、植入物设计以及术后关节功能评估中的应用。检索了 Web of Science、Scopus、Medline、Embase、Global Health 和 Central 等数据库。53篇相关文章符合纳入标准,并采用修改后的Downs和Black评价标准框架对其质量进行了评估。从计算机断层扫描中手动分割图像耗时较长,目前仍是生成髋骨三维模型的主要方法;然而,统计形状模型一旦开发出来,可用于快速估算缺损前的解剖结构。有限元建模可用于估算骨应力和应变以及术后植入物的微动,在定制和现成植入物设计、减轻应力屏蔽以及预测骨重塑和植入物稳定性方面发挥了关键作用。然而,模型验证具有挑战性,需要对中长期临床结果进行严格的评估和比较。开发快速、准确的髋臼缺陷建模方法,包括统计形状模型和人工神经网络,最终可能会提高研究和临床对 rTHA 建模和仿真的接受度,并扩大其应用范围。
期刊介绍:
The Journal of Orthopaedic Research is the forum for the rapid publication of high quality reports of new information on the full spectrum of orthopaedic research, including life sciences, engineering, translational, and clinical studies.