Abstract
Background
High tibial osteotomy (HTO) is a knee preservation procedure used to treat osteoarthritis
of the knee. Identifying the hinge point, surgical point, and Fujisawa point in the
patient’s knee X-ray before surgery is a critical task. The aim of this study was
to propose an artificial intelligence-based method to effectively help surgeons select
the location of these landmark points, which provides important reference for subsequent
surgery.
Methods
We proposed PFDNet (parallel fusion decoding network), a novel convolutional neural
network for key-point estimation of knee X-rays. PFDNet employs Res2Net for feature
extraction in the network encoding phase and two partial decoders connected in parallel
in the network decoding phase to finely aggregate the multiscale feature information
produced by Res2Net. A total of 1842 knee X-ray images were trained, validated and
predicted by PFDNet to determine whether the network could accurately detect key-points
in the HTO surgical plan.
Results
At the hinge point, surgical point, and Fujisawa point, the average error and standard
deviation from the calibration value in the PFDNet test results were 2.06 ± 1.165 mm, 2.713 ± 1.457 mm, and 2.015 ± 1.304 mm, respectively. This method exhibits superior performance compared with four
convolutional neural network models that are also based on encoding and decoding frameworks:
U-Net, ResUnet, SegNet, and FCN.
Conclusion
The hinge point, surgical point, and Fujisawa point can be clearly selected by PFDNet
from knee X-ray images and is locked to the millimeter level. The results show that
the proposed artificial intelligence-based strategy can be instrumental in preoperative
HTO planning.
Keywords
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Article info
Publication history
Published online: December 16, 2022
Accepted:
November 30,
2022
Received in revised form:
November 8,
2022
Received:
June 13,
2022
Identification
Copyright
© 2022 Elsevier B.V. All rights reserved.
