In this section
@article{Burge:2023:10.1038/s41598-023-30483-5,
author = {Burge, T and Jeffers, J and Myant, C},
doi = {10.1038/s41598-023-30483-5},
journal = {Scientific Reports},
pages = {1--9},
title = {Applying machine learning methods to enable automatic customisation of knee replacement implants from CT data},
url = {http://dx.doi.org/10.1038/s41598-023-30483-5},
volume = {13},
year = {2023}
}
TY - JOUR
AB - The aim of this study was to develop an automated pipeline capable of designing custom total knee replacement implants from CT scans. The developed pipeline firstly utilised a series of machine learning methods including classification, object detection, and image segmentation models, to extract geometrical information from inputted DICOM files. Statistical shape models then used the information to create femur and tibia 3D surface model predictions which were ultimately used by computer aided design scripts to generate customised implant designs. The developed pipeline was trained and tested using CT scan images, along with segmented 3D models, obtained for 98 Korean Asian subjects. The performance of the pipeline was tested computationally by virtually fitting outputted implant designs with ‘ground truth’ 3D models for each test subject’s bones. This demonstrated the pipeline was capable of repeatably producing highly accurate designs, and its performance was not impacted by subject sex, height, age, or knee side. In conclusion, a robust, accurate and automatic, CT-based total knee replacement customisation pipeline was shown to be feasible and could afford significant time and cost advantages over conventional methods. The pipeline framework could also be adapted to enable customisation of other medical implants.
AU - Burge,T
AU - Jeffers,J
AU - Myant,C
DO - 10.1038/s41598-023-30483-5
EP - 9
PY - 2023///
SN - 2045-2322
SP - 1
TI - Applying machine learning methods to enable automatic customisation of knee replacement implants from CT data
T2 - Scientific Reports
UR - http://dx.doi.org/10.1038/s41598-023-30483-5
UR - https://www.nature.com/articles/s41598-023-30483-5
UR - http://hdl.handle.net/10044/1/103488
VL - 13
ER -
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