@article{Burge:2023:10.1007/s00170-023-11357-6,
author = {Burge, TA and Munford, MJ and Kechagias, S and Jeffers, JRT and Myant, CW},
doi = {10.1007/s00170-023-11357-6},
journal = {The International Journal of Advanced Manufacturing Technology},
title = {Automating the customization of stiffness-matched knee implants using machine learning techniques},
url = {http://dx.doi.org/10.1007/s00170-023-11357-6},
year = {2023}
}

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TY  - JOUR
AB  - 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.
AU  - Burge,TA
AU  - Munford,MJ
AU  - Kechagias,S
AU  - Jeffers,JRT
AU  - Myant,CW
DO  - 10.1007/s00170-023-11357-6
PY  - 2023///
SN  - 0268-3768
TI  - Automating the customization of stiffness-matched knee implants using machine learning techniques
T2  - The International Journal of Advanced Manufacturing Technology
UR  - http://dx.doi.org/10.1007/s00170-023-11357-6
ER  - 

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