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Wear patterns in knee OA correlate with native limb geometry

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Abstract
Background: To date, the amount of cartilage loss is graded by means of discrete scoring systems on artificially divided regions of interest (ROI). However, optimal statistical comparison between and within populations requires anatomically standardized cartilage thickness assessment. Providing anatomical standardization relying on non-rigid registration, we aim to compare morphotypes of a healthy control cohort and virtual reconstructed twins of end-stage knee OA subjects to assess the shape-related knee OA risk and to evaluate possible correlations between phenotype and location of cartilage loss. Methods: Out of an anonymized dataset provided by the Medacta company (Medacta International SA, Castel S. Pietro, CH), 798 end-stage knee OA cases were extracted. Cartilage wear patterns were observed by computing joint space width. The three-dimensional joint space width data was translated into a two-dimensional pixel image, which served as the input for a principal polynomial autoencoder developed for non-linear encoding of wear patterns. Virtual healthy twin reconstruction enabled the investigation of the morphology-related risk for OA requiring joint arthroplasty. Results: The polynomial autoencoder revealed 4 dominant, orthogonal components, accounting for 94% of variance in the latent feature space. This could be interpreted as medial (54.8%), bicompartmental (25.2%) and lateral (9.1%) wear. Medial wear was subdivided into anteromedial (11.3%) and posteromedial (10.4%) wear. Pre-diseased limb geometry had a positive predictive value of 0.80 in the prediction of OA incidence (r 0.58, p < 0.001). Conclusion: An innovative methodological workflow is presented to correlate cartilage wear patterns with knee joint phenotype and to assess the distinct knee OA risk based on pre-diseased lower limb morphology. Confirming previous research, both alignment and joint geometry are of importance in knee OA disease onset and progression.
Keywords
Biomedical Engineering, Histology, Bioengineering, Biotechnology, knee wear, alignment, osteoarthritis, knee diagnostic imaging, statistical shape analysis

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MLA
Van Oevelen, Aline, et al. “Wear Patterns in Knee OA Correlate with Native Limb Geometry.” FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY, vol. 10, Frontiers Media SA, 2022, doi:10.3389/fbioe.2022.1042441.
APA
Van Oevelen, A., Van den Borre, I., Duquesne, K., Pizurica, A., Victor, J., Nauwelaers, N., … Audenaert, E. (2022). Wear patterns in knee OA correlate with native limb geometry. FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY, 10. https://doi.org/10.3389/fbioe.2022.1042441
Chicago author-date
Van Oevelen, Aline, Ide Van den Borre, Kate Duquesne, Aleksandra Pizurica, Jan Victor, N. Nauwelaers, P. Claes, and Emmanuel Audenaert. 2022. “Wear Patterns in Knee OA Correlate with Native Limb Geometry.” FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY 10. https://doi.org/10.3389/fbioe.2022.1042441.
Chicago author-date (all authors)
Van Oevelen, Aline, Ide Van den Borre, Kate Duquesne, Aleksandra Pizurica, Jan Victor, N. Nauwelaers, P. Claes, and Emmanuel Audenaert. 2022. “Wear Patterns in Knee OA Correlate with Native Limb Geometry.” FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY 10. doi:10.3389/fbioe.2022.1042441.
Vancouver
1.
Van Oevelen A, Van den Borre I, Duquesne K, Pizurica A, Victor J, Nauwelaers N, et al. Wear patterns in knee OA correlate with native limb geometry. FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY. 2022;10.
IEEE
[1]
A. Van Oevelen et al., “Wear patterns in knee OA correlate with native limb geometry,” FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY, vol. 10, 2022.
@article{01GMQDBHAPJAP8RVXXP0NE6FKJ,
  abstract     = {{Background: To date, the amount of cartilage loss is graded by means of discrete scoring systems on artificially divided regions of interest (ROI). However, optimal statistical comparison between and within populations requires anatomically standardized cartilage thickness assessment. Providing anatomical standardization relying on non-rigid registration, we aim to compare morphotypes of a healthy control cohort and virtual reconstructed twins of end-stage knee OA subjects to assess the shape-related knee OA risk and to evaluate possible correlations between phenotype and location of cartilage loss. Methods: Out of an anonymized dataset provided by the Medacta company (Medacta International SA, Castel S. Pietro, CH), 798 end-stage knee OA cases were extracted. Cartilage wear patterns were observed by computing joint space width. The three-dimensional joint space width data was translated into a two-dimensional pixel image, which served as the input for a principal polynomial autoencoder developed for non-linear encoding of wear patterns. Virtual healthy twin reconstruction enabled the investigation of the morphology-related risk for OA requiring joint arthroplasty. Results: The polynomial autoencoder revealed 4 dominant, orthogonal components, accounting for 94% of variance in the latent feature space. This could be interpreted as medial (54.8%), bicompartmental (25.2%) and lateral (9.1%) wear. Medial wear was subdivided into anteromedial (11.3%) and posteromedial (10.4%) wear. Pre-diseased limb geometry had a positive predictive value of 0.80 in the prediction of OA incidence (r 0.58, p < 0.001). Conclusion: An innovative methodological workflow is presented to correlate cartilage wear patterns with knee joint phenotype and to assess the distinct knee OA risk based on pre-diseased lower limb morphology. Confirming previous research, both alignment and joint geometry are of importance in knee OA disease onset and progression.}},
  articleno    = {{1042441}},
  author       = {{Van Oevelen, Aline and Van den Borre, Ide and Duquesne, Kate and Pizurica, Aleksandra and Victor, Jan and Nauwelaers, N. and Claes, P. and Audenaert, Emmanuel}},
  issn         = {{2296-4185}},
  journal      = {{FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY}},
  keywords     = {{Biomedical Engineering,Histology,Bioengineering,Biotechnology,knee wear,alignment,osteoarthritis,knee diagnostic imaging,statistical shape analysis}},
  language     = {{eng}},
  pages        = {{13}},
  publisher    = {{Frontiers Media SA}},
  title        = {{Wear patterns in knee OA correlate with native limb geometry}},
  url          = {{http://doi.org/10.3389/fbioe.2022.1042441}},
  volume       = {{10}},
  year         = {{2022}},
}

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