Total joint replacement has excellent long-term outcomes but new designs are difficult to assess due to patient variability. The speaker discusses moving towards population-based computational modeling to generate hundreds of subject-specific models to better evaluate implant performance. Principal component analysis is used to build statistical shape models of femurs to automatically generate new instances for implant positioning and finite element analysis. This allows evaluation of variability across a population rather than a single "average" patient.
1. Moving Towards Population
Based Computational
Modelling of Total Joint
Replacement
Professor Mark Taylor
2. Total Joint Replacement
Excellent survivorship at 10
years
New designs regularly enter
the market
Increasingly difficult to
assess whether design
changes will improve
performance
3. Sources of Variability
The Patient Surgery
•Experience
•Personal preference
•Age/activity level
•Alignment
•Bone quality/geometry
•Surgical approach
•Soft tissue quality
•Body weight
4. Femoral Head Resurfacing
Initial early-mid term clinical
results impressive
However:
High incidence of femoral
neck fracture in first 6
months
5 fold increase in revision
rate in small diameter heads
as compared to large
diameter heads1 http://www.orthoassociates.com
1Shimmin et al, JBJS(Br), 2010
5. FE analysis of the
resurfaced femoral head:
Modelling of an individual
patient
7. Subject specific models
- Significant strain
shielding within the
head
- Increase in strain
on the superior
aspect of the neck
- Peak strain occurs
around the inferior
aspect of the neck
8. Comparison of a small vs. large femur
Small femur Large femur
9. Typical FE analysis of the resurfaced
femoral head
Typically model the
“average” patient
Ideal implantation, single
size
Parametric studies on limited
number of variables
Attempt to extrapolate results
to larger patient population
Patient variability swamps
differences?
10. Typical FE analysis of the resurfaced
femoral head
Typically model the
“average” patient
Ideal implantation, single
size will not predict small percentage of failures
This
Parametric studies on limited
Radical re-think of pre-clinical testing needed!
number of variables
Attempt to extrapolate results
to larger patient population
Patient variability swamps
differences?
11. FE analysis of the
resurfaced femoral head:
Modelling of 10’s of
patients
12. The brute force approach
- Model multiple femurs
from a range of patients
- Examine mean, standard
xN deviation, range….
- Perform statistical tests
when comparing designs
Radcliffe et al, Clin. Biomech., 2007
13. The brute force approach
Patient Data
200 45
180 40
160
Height (cm) / Weight
35
140
30
120
25
(kg)
BMI
100
20
80
15
60
40 10
20 5
0 0
Hip 609 Hip 613 Hip 628 Hip 631 Hip 636 Hip 608 Hip 626 Hip 607 Hip 625 Hip 612 Hip 610 Hip 630 Hip 614 Hip 635 Hip 627 Hip 634
Hip Number
Height (cm) Weight (kg) BMI
Weight: 95.312 kg (54 – 136)
Height: 1.76 m (1.57 – 1.88)
Age: 40.75 years (18 – 57)
Gender: male dominated
17. The brute force approach
N=16
- Very labour intensive
-Impractical to examine 100’s of
femurs
- Still difficult to compare differences
across sizes
Radcliffe et al, PhD Thesis, 2007
18. FE analysis of the
resurfaced femoral head:
Modelling of 100’s of
patients
20. Statistical Shape and Intensity Model (n=46)
Mode 1 – Scaling of
morphology and properties
Mode 2 – Scaling and neck
anteversion
Model 3 – Neck anteversion
and head/neck ratio
Bryan et al, Med. Eng.
Phys., 2010
21. Generation of New Instances
• Using governing PCA equation it is possible to generate new,
realistic femur models from the variations captured by the
model
26. Results - Comparison between head sizes
N=20
N=25
Small diameter heads show: Bryan et al, J. Biomech., 2012
- Increased strain shielding
- Elevated strains at the superior femoral neck
27. Statistical Shape and Intensity Model
• Developed methodology has
significant potential for improving
preclinical assessment
• There are issues:
• Statistical shape and intensity
models only as good as the
training set
• Robust automation
• Forces may need to link with
musculoskeletal models
• Verification/validation