## 4 Discussion
Implant size selection based on manual 3D templating with MRI scans has been shown to have excellent intra- and interclass correlation coefficients amongst surgeons [36]. The intra-class correlation ranged from 96.6% to 99%, and the inter-class correction was 97%. Hence, it is a suitable medium for preoperative assessment of the required implant size. To the best of our knowledge there is only one study which investigated an automatic 2D templating method with 19.2% accuracy [37]. For our dataset, we can compare the accuracy of the model with that of the femoral implant size proposed by the instrumentation manufacturer. The test set accuracy of the femoral implant size proposed by the manufacturer is 23.1%, while our model obtained an accuracy of 70.08%. This accuracy improvement will result in a more efficient planning experience for the surgeon by reducing planning time. However, both the manufacturer's default and our method obtain the same test set accuracy up to one size larger or smaller of 99.11%. Furthermore, our proposed method also significantly surpasses the baseline method in terms of accuracy. Our method outperforms other methods relying on demographic data in terms of both the absolute accuracy and the accuracy up to 1 size smaller or larger (Table 3). Therefore, we conclude that geometric features extracted from the 3D anatomical model provide additional information that improves implant size prediction accuracy. Furthermore, all other investigated methods rely on features defined based on clinical expertise. In comparison, our method automatically extracts the relevant features which determine the femoral implant size. The study by [PERSON] et al., (2022) obtained a higher absolute accuracy compared to our currently proposed method, however, it relied on a significant amount of clinical knowledge to extract relevant features from the 3D bone models and uses non-linear models ([PERSON] et al., 2022). One of the limitations of this comparison is the difference in focus: our method predicts the preoperatively planned implant size, which differs slightly from the intra-operatively used implant size predicted by the other studies relying on demographic data. More accurate implant size prediction using our method might positively impact total knee arthroplasty surgery through improved logistics requiring reduced inventory, less trays to sterilize and reduced operating room setup times. These measures might reduce the cost associated with the treatment. Furthermore, accurate templating helps the surgeon to be better prepared for surgery and can potentially reduce the operative times. Nevertheless, these claims need to be further validated. The advantages of our model are its fast calculation time and the ability to produce accurate results when limited data are available. However, there are also points of improvement. The proposed method was validated for a single surgeon. Hence, further research can be conducted to investigate generalization to a larger population of surgeons. Additional research could look into combining the proposed mesh-based features with relevant demographic data to improve model accuracy. Another limitation is the linearity implied by our model. Possibly, non-linearities could provide higher accuracy through interaction of the vertex coordinates, such as distances or angles. Finally, it could be investigated if this \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Study** & **Absolute Accuracy** & **+1/\(-\)1 Size Accuracy** & **Modality** \\ \hline [PERSON] et al. 2009 [3] & 48\% & 98\% & 2D: X-ray \\ [PERSON] et al. 2012 [4] & 64\% & **100\%** & 2D: X-ray \\ [PERSON] et al. 2007 [5] & 50.4\% & **97.3\%** & 2D: X-ray \\ [PERSON] et al. 2019 [6] & 52.9\% & - & 2D: X-Ray \\ [PERSON] et al. 2016 [7] & 59.6\% & 97.9\% & 2D: X-ray \\ [PERSON] et al. 2019 [6] & 96.6\% & - & 3D: CT \\ [PERSON] et al. 2016 [7] & **100\%** & **100\%** & 3D: MRI \\ [PERSON] et al. 2016 [8] & 93.9\% & - & 3D: MRI \\ \hline \hline \end{tabular}
\end{table}