# Using Statistical Shape Models to Optimize TKA Implant Design
## 2 Materials and Methods  
### Generation of the SSM  
#### 2.1.1 Selection of Input Data  
Beside the methodology used to create the SSM, two main factors influence the quality of a SSM: the number of datasets used and the homogeneity of the studied population. It is therefore important to balance those two factors when selecting the pool of data used to build a model: the more homogeneous the group, the better the model will represent it. The more morphologies are included in the model, the better it will represent a large portion of the population. Therefore, for each application, the input population must be defined with care. When building a SSM to evaluate the fit of TKA implants, it has to be considered whether the input population should be limited to osteoarthritis (OA) patients, or if the model would benefit from including healthy patients as well. Consideration should also be given to whether different SSMs should be generated for each gender and ethnicity, or whether a global model will suffice.
* Impact of pathology: arthritic versus healthy population. In a preliminary study, the following three SSMs were built: The 'OA-SSM' was built from CT scans of 40 arthritic patients (24 female, 16 male, mean age: 68 \(\pm\) 10 years; range 46-91 years). Patients with Kellgren-Lawrence grades one to three [19] were included. Severe deformities were excluded. The 'healthy SSM' was built from CT scans of 40 patients (20 female, 20 male, mean age: 65 \(\pm\) 14 years, range 29-87 years) with no known knee disorder. The 'combined SSM' was built from all the healthy and OA CT scans described above (80 patients, 44 female, 36 male, mean age: 67 \(\pm\) 12 years, range 29-91 years). The CT scans were obtained from different hospitals across Europe. The average models of the cortical femur, cortical tibia, and cortical patella of each population (OA, healthy and combined) were compared using two methods. First, the SSMs of the OA and healthy population were aligned to each other by minimizing the root mean square (RMS) error between the two models. This RMS error along with the maximum deviations between the SSMs were used to compare both SSMs. This analysis was performed for all three bones (femur, tibia, patella) via a numerical color map that showed the RMS error between the mesh points of each model (Materialise(r) Mimics Innovation Suite Software). Second, to gain insight into how differently the OA and healthy populations behave when they are part of the combined population, Student's T-test was performed on the mode coefficients of the combined SSM: If the OA and healthy populations were similar, the mode coefficients of the combined SSM would not be statistically different from the mode coefficients of the healthy and OA SSMs.
* Impact of gender and ethnicity. The impact of mixing genders and ethnicities in different models was evaluated as follows: 92 CT scans from healthy Caucasian and Asian male and female patients were used. One SSM was generated out of the 92 datasets. The CT scans were divided in four equal subgroups according to the ethnicity and gender of the patients (23 CT scans for each group): Caucasian male, Asian male, Asian female, and Caucasian female. One SSM was built for each subgroup, resulting in 4 distinct SSMs. The first mode of variation was used to define different sizes of the model: 5 th, 50 th and 95 th percentile. Each SSM was used to rebuild the anatomy of another subgroup. Based on these preliminary studies, a Caucasian SSM was built from 120 anatomies (65 female, 55 male) and an Asian one was built from 112 patients (75 female, 37 male).