Automating the customization of stiffness-matched knee implants using machine learning techniques
Automating the customization of stiffness-matched knee implants using machine learning techniques
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DOI：
10.1007/s00170-023-11357-6
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发表时间：
2023-04-10
影响因子：
3.4
通讯作者：
Myant, Connor W.
中科院分区：
文献类型：
--
作者：
Burge, Thomas A.;Munford, Maxwell J.;Myant, Connor W.
在膝关节置换术中，植入物用于置换胫骨和股骨的关节面，其中大部分由实心金属部件构成。因此，生物力学应力和应变不再充分分布在关节术后，阻止有益的骨重建。为了缓解这种情况，研究探索了增材制造具有多孔晶格结构的植入物，以匹配骨的机械性能。作者还概述了如何使用计算机断层扫描数据来设计这种结构，以模拟个人骨骼的刚度。然而，此类方法目前需要训练有素的专业人员进行大量手工工作来处理图像文件、提取密度信息并设计晶格结构。这项研究提出了被认为是第一个能够生产胫骨托的全自动流水线，该胫骨托具有专门为个人骨骼定制的顺应性结构，使用机器学习方法实现。新的过程，结合分类，对象检测和分割机器学习模型，用于促进自动化的工作流程，概述。然后通过使用临床计算机断层扫描数据进行测试并将结果与手动获得的结果进行比较来证明管道的有效性。作为概念验证，还制造了由流水线生成的具有不同复杂程度的原型设计，直到并包括通过胫骨轴在3D中映射刚度变化。
In knee arthroplasty, implants are used to replace the articulating surfaces of the tibia and femur bones, with most constituting of solid metallic components. Consequentially, biomechanical stresses and strains are no longer adequately distributed at the joint post-surgery, preventing beneficial bone remodeling. To mitigate this studies have explored additively manufacturing implants with porous lattice structures to match the mechanical properties of bone. Authors have also outlined how such structures can be designed using computed tomography data to simulate the stiffness of individuals' bones. Such methods however currently require substantial manual work by trained professionals to process the image files, extract the density information, and design lattice structures. This study proposes what is believed to be the first fully automatic pipeline capable of producing tibial trays with compliant structures customized specifically for individuals' bones, achieved using machine learning methods. The novel process, combining classification, object detection, and segmentation machine learning models, used to facilitate the automated workflow, is outlined. The efficaciousness of the pipeline is then demonstrated by testing it using clinical computed tomography data and comparing the results with those obtained manually. As a proof of concept, prototype designs generated by the pipeline with differing degrees of complexity, up to and including mapping stiffness variation in 3D through the shaft of the tibia, were also fabricated.