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Development of an Automated Mass-Customization Pipeline for Knee Replacement Surgery Using Biplanar X-Rays.

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Title: Development of an Automated Mass-Customization Pipeline for Knee Replacement Surgery Using Biplanar X-Rays.
Authors: Burge, Thomas A.1 t.burge20@imperial.ac.uk, Jeffers, Jonathan R. T.2 j.jeffers@imperial.ac.uk, Myant, Connor W.1 connor.myant@imperial.ac.uk
Source: Journal of Mechanical Design. Feb2022, Vol. 144 Issue 2, p1-11. 11p.
Subjects: Total knee replacement, Knee surgery, Knee, Magnetic resonance imaging, Convolutional neural networks, X-ray imaging
Abstract: For standard "off-the-shelf" knee replacement procedures, surgeons use X-ray images to aid implant selection from a limited number of models and sizes. This can lead to complications and the need for implant revision due to poor implant fit. Customized solutions have been shown to improve results but require increased preoperative assessment (Computed Tomography or Magnetic Resonance Imaging), longer lead times, and higher costs which have prevented widespread adoption. To attain the benefits of custom implants, whilst avoiding the limitations of currently available solutions, a fully automated mass-customization pipeline, capable of developing customized implant designs for fabrication via additive manufacturing from calibrated X-rays, is proposed. The proof-of-concept pipeline uses convolutional neural networks to extract information from biplanar X-ray images, point depth, and statistical shape models to reconstruct the anatomy, and application programming interface scripts to generate various customized implant designs. The pipeline was trained using data from the Korea Institute of Science and Technology Information. Thirty subjects were used to test the accuracy of the anatomical reconstruction, ten from this data set, and a further 20 independent subjects obtained from the Osteoarthritis Initiative. An average root-mean-squared error of 1.00 mm was found for the femur test cases and 1.07 mm for the tibia. Three-dimensional (3D) distance maps of the output components demonstrated these results corresponded to well-fitting components, verifying automatic customization of knee replacement implants is feasible from 2D medical imaging. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Mechanical Design is the property of American Society of Mechanical Engineers and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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PubType: Academic Journal
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  Data: Development of an Automated Mass-Customization Pipeline for Knee Replacement Surgery Using Biplanar X-Rays.
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  Data: <searchLink fieldCode="AR" term="%22Burge%2C+Thomas+A%2E%22">Burge, Thomas A.</searchLink><relatesTo>1</relatesTo><i> t.burge20@imperial.ac.uk</i><br /><searchLink fieldCode="AR" term="%22Jeffers%2C+Jonathan+R%2E+T%2E%22">Jeffers, Jonathan R. T.</searchLink><relatesTo>2</relatesTo><i> j.jeffers@imperial.ac.uk</i><br /><searchLink fieldCode="AR" term="%22Myant%2C+Connor+W%2E%22">Myant, Connor W.</searchLink><relatesTo>1</relatesTo><i> connor.myant@imperial.ac.uk</i>
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Mechanical+Design%22">Journal of Mechanical Design</searchLink>. Feb2022, Vol. 144 Issue 2, p1-11. 11p.
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  Data: <searchLink fieldCode="DE" term="%22Total+knee+replacement%22">Total knee replacement</searchLink><br /><searchLink fieldCode="DE" term="%22Knee+surgery%22">Knee surgery</searchLink><br /><searchLink fieldCode="DE" term="%22Knee%22">Knee</searchLink><br /><searchLink fieldCode="DE" term="%22Magnetic+resonance+imaging%22">Magnetic resonance imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Convolutional+neural+networks%22">Convolutional neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22X-ray+imaging%22">X-ray imaging</searchLink>
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  Label: Abstract
  Group: Ab
  Data: For standard "off-the-shelf" knee replacement procedures, surgeons use X-ray images to aid implant selection from a limited number of models and sizes. This can lead to complications and the need for implant revision due to poor implant fit. Customized solutions have been shown to improve results but require increased preoperative assessment (Computed Tomography or Magnetic Resonance Imaging), longer lead times, and higher costs which have prevented widespread adoption. To attain the benefits of custom implants, whilst avoiding the limitations of currently available solutions, a fully automated mass-customization pipeline, capable of developing customized implant designs for fabrication via additive manufacturing from calibrated X-rays, is proposed. The proof-of-concept pipeline uses convolutional neural networks to extract information from biplanar X-ray images, point depth, and statistical shape models to reconstruct the anatomy, and application programming interface scripts to generate various customized implant designs. The pipeline was trained using data from the Korea Institute of Science and Technology Information. Thirty subjects were used to test the accuracy of the anatomical reconstruction, ten from this data set, and a further 20 independent subjects obtained from the Osteoarthritis Initiative. An average root-mean-squared error of 1.00 mm was found for the femur test cases and 1.07 mm for the tibia. Three-dimensional (3D) distance maps of the output components demonstrated these results corresponded to well-fitting components, verifying automatic customization of knee replacement implants is feasible from 2D medical imaging. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Journal of Mechanical Design is the property of American Society of Mechanical Engineers and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1115/1.4052192
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      – SubjectFull: Knee surgery
        Type: general
      – SubjectFull: Knee
        Type: general
      – SubjectFull: Magnetic resonance imaging
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      – SubjectFull: Convolutional neural networks
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      – TitleFull: Development of an Automated Mass-Customization Pipeline for Knee Replacement Surgery Using Biplanar X-Rays.
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              Text: Feb2022
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