Automatic Friedman’s Axis placement via the use of deep learning algorithms

Clément Daviller; Sandrine V. Polakovic; Alexander T. Greene; Fabrice Bertrand

doi:10.29007/r8cp

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Automatic Friedman’s Axis placement via the use of deep learning algorithms

4 pages•Published: December 13, 2022

Clément Daviller, Sandrine V. Polakovic, Alexander T. Greene and Fabrice Bertrand

Abstract

Reference axis based on Friedman’s approach is widely recognized as an anatomic landmark from which to measure and compare implant parameters within preoperative planning software for total shoulder arthroplasty. Equinoxe Planning Application (ExactechInc.) offers 3D measurements techniques for glenoid version and inclination requiring meticulous placement of trigonum and glenoid center. We propose as automatic determination of this reference axis, based on deep learning that shown a median error of less than 1°.

Keyphrases: Convolutional Neural Network, CT-guided Arthroplasty, deep learning, Friedman Axis, machine learning, Total Shoulder Arthroplasty

In: Ferdinando Rodriguez Y Baena, Joshua W Giles and Eric Stindel (editors). Proceedings of The 20th Annual Meeting of the International Society for Computer Assisted Orthopaedic Surgery, vol 5, pages 37--40

Links:	https://easychair.org/publications/paper/dLgC
	https://doi.org/10.29007/r8cp

BibTeX entry

@inproceedings{CAOS2022:Automatic_Friedmans_Axis_placement,
  author    = {Cl\textbackslash{}'ement Daviller and Sandrine V. Polakovic and Alexander T. Greene and Fabrice Bertrand},
  title     = {Automatic Friedman's Axis placement via the use of deep learning algorithms},
  booktitle = {Proceedings of The 20th Annual Meeting of the International Society for Computer Assisted Orthopaedic Surgery},
  editor    = {Ferdinando Rodriguez Y Baena and Joshua W Giles and Eric Stindel},
  series    = {EPiC Series in Health Sciences},
  volume    = {5},
  pages     = {37--40},
  year      = {2022},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-5305},
  url       = {https://easychair.org/publications/paper/dLgC},
  doi       = {10.29007/r8cp}}

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