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Instantaneous Generation of Subject-Specific Finite Element Models of the Hip Capsule.

Ahilan Anantha-KrishnanCasey A MyersClare K FitzpatrickChadd W Clary
Published in: Bioengineering (Basel, Switzerland) (2023)
Subject-specific hip capsule models could offer insights into impingement and dislocation risk when coupled with computer-aided surgery, but model calibration is time-consuming using traditional techniques. This study developed a framework for instantaneously generating subject-specific finite element (FE) capsule representations from regression models trained with a probabilistic approach. A validated FE model of the implanted hip capsule was evaluated probabilistically to generate a training dataset relating capsule geometry and material properties to hip laxity. Multivariate regression models were trained using 90% of trials to predict capsule properties based on hip laxity and attachment site information. The regression models were validated using the remaining 10% of the training set by comparing differences in hip laxity between the original trials and the regression-derived capsules. Root mean square errors (RMSEs) in laxity predictions ranged from 1.8° to 2.3°, depending on the type of laxity used in the training set. The RMSE, when predicting the laxity measured from five cadaveric specimens with total hip arthroplasty, was 4.5°. Model generation time was reduced from days to milliseconds. The results demonstrated the potential of regression-based training to instantaneously generate subject-specific FE models and have implications for integrating subject-specific capsule models into surgical planning software.
Keyphrases
  • finite element
  • total hip arthroplasty
  • minimally invasive
  • virtual reality
  • healthcare
  • climate change
  • anterior cruciate ligament reconstruction
  • quality improvement
  • breast cancer risk