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Machine learning identifies factors related to early joint space narrowing in dysplastic and non-dysplastic hips.

Michail E KlontzasEmmanouil VolitakisÜstün AydingözKonstantinos ChlapoutakisApostolos H Karantanas
Published in: European radiology (2021)
• Neither anterior nor lateral acetabular dysplasia was sufficient to independently reduce joint space width in a multivariate linear regression model of dysplastic hips. • A random forest classifier was developed based on measurements and demographic parameters from 507 hip joints, achieving an area under the curve of 69.9% in the external validation set, in predicting severe joint space narrowing based on anatomical hip parameters and age. • Unsupervised TwoStep cluster analysis revealed two distinct population groups, one with low and one with normal joint space width, characterised by differences in hip morphology.
Keyphrases
  • machine learning
  • total hip arthroplasty
  • climate change
  • genome wide
  • early onset
  • gene expression
  • minimally invasive
  • deep learning
  • data analysis
  • big data
  • drug induced