1. Vidhya sundhararaj 2115580 Supervisor Prof Mark Taylor

2.  Population based studies are important for implant study, risk assessment for fracture, pre-clinical studies.  Modelling of single femur or limited number of femur excludes inter-patient variability and extrapolation to population makes less sensible. Also creating multiple models is time consuming.  Statistical modelling overcomes this issue of model generation.

3. To statistically model femur that represents maximum variation in femur population in terms of bone geometry, material property and forces and analysing if force can predict geometry and material property.

4. 1 PCA on Forces 2 PCA on simple geometry 3 PCA on registered geometry 5 PCA with Density 1 Regression on force and simple geometry 2 Regression on force and registered geometry 3 Regression on force and density 4 PCA on surface nodes

5.  A data reduction method that accounts for most of the variation in the original data. The obtained variables are Called principal components & are uncorrelated to each other.

6. Time for stance was obtained from musculoskeletal models in OpenSim. Muscolo skeletal models developed by Saulo martelli

7.  26 muscle forces On femur and hip joint Forces.

8. 9 modes 36.1185142482350 49.0151734147590 60.9275970287362 69.0040742900684 75.6375736855110 81.4136554583573 85.3248586585143 89.0679714765587 91.4560197304632

9.  Surface matching – deforms the baseline surface to match the given target surface. Volume morphing- creates the internal mesh points based on surface nodes.

10. 6 modes 49.1722858737894 73.5022024924462 82.4879247417899 89.6331203177221 92.5821919508897 95.388699332539 6 modes 46.095523527846 72.063448429335 80.6772566973216 88.1151404184275 92.2940278597457 95.0421274638311

11. 9 modes 29.5581178678154 48.2866868347216 61.5598539541340 71.8252523911785 80.9403289449587 88.2955863787843 91.9716698668447 93.7112395356659 95.2916843891132

12.  Analysis was performed on force and shape data to know if shape can predict force.  Scatter plots of force and shape

13.  Regression – a measure of relationship between variable. Has independent variable as predictors and dependent variable as outcome or response variable.  Multiple regression analysis – more than one predictor to predict outcome.  Stepwise regression – shows significant variables that can predict outcome.

14. X1- Head radius X2- neck major radius X3- neck minor radius y- mean peak forces Fx

15. Regstat function – performs multiple regression by fitting model to the data. ‘linear‘ - Includes constant and linear terms (default). 'interaction‘-Includes constant, linear, and cross product terms. Mode 1 force represented by shape modes Mode 2 force represented by shape modes

16. Interaction model Mode 1 of force Mode 2 of force

17. MODE 1 FORCE REPRESENTED BY SHAPE MODES MODE 2 FORCE REPRESENTED BY SHAPE MODES

18. TERMS ADDED TERMS REMOVED

19. FOR FORCE

20.  Interpretation of force and shape modes  Volume morphing.  Density extraction from CT scans and assigning it to meshes.  PCA and Regression analysis

21. Questions