# Adaptive Point Learning with Uncertainty Quantification to Generate Margin Lines on Prepared Teeth
## 5 Discussion
On the other hand, it will not be dangerous if our model predicts a non-confident score for a good margin line. In other words, minimizing the false negative is not as important as minimizing the false positive. The slight deviation between the confidence metric and CD is because we established the confidence metric based on a strict boolean. Thresholds could be controlled by end users to be more or less conservative. In addition, the correlation metric values for the validation set (Table 5) were slightly lower than the test set (Table 4) due to the presence of more difficult cases in the validation set. In addition, the number of cases in the test set (i.e., 134) was lower than the validation set (i.e., 183). The ablation study presented in Figure 8 demonstrated the effect of our novel technique of outlier removal on the final prediction of the margin line. Compared to the baseline model, which predicted a spline without removing outliers, the spline prediction after removing outliers showed a significant improvement. The spline prediction, representing the margin line, aligned more closely with the ground truth after the removal of outliers. Our five-fold cross-validation also showed consistent low values of the percentages of outliers identified as shown in Table 3 with all values being equal to or less than 0.13%. Despite the advancement reported in the current study, it has some limitations. The first one is related to the time needed to obtain a prediction using the combined model, which is five times higher than a single model. The approximate time to generate a prediction, remove outliers, and turn it into a spline from one input is around 17 s. This includes 9 s to load the model and data, 6 s to remove outliers, and 2 s to make a spline. This time could be reduced using a multi-threading technique during deployment, which is currently in progress. Overall, the model accuracy and efficiency are expected to improve by training on a larger and more diverse dataset. The solution proposed is aimed to assist dental technicians in extracting the margin line. We decided to output the spline as a point cloud but also as a.json file containing the parameters of the spline. As such, this tool could be integrated into an API with a user interface that allows the user to move the control points of the spline to make modification easily and adjust the margin line if needed. In addition, the margin line extraction tool will be integrated with a crown generation deployed at [[https://app.intellidentia.com/](https://app.intellidentia.com/)]([https://app.intellidentia.com/](https://app.intellidentia.com/)) Intellidentia.com. Future work should consider the integration of the confidence metric in the loss function during training and the effect of that on the model accuracy and efficiency. In addition, methods of continuous learning based on expert feedback during deployment should be investigated. Recent strides have been made in the application of deep reinforcement learning in medical and dental imaging [61; 62; 63]. Future work can focus on integrating reinforcement learning such that feedback from deployment could be used to enhance the predictions of the model. Comparing the proposed confidence metric with alternative uncertainty quantification techniques should also be explored. Finally, we expect that implementing more sophisticated augmentation techniques will improve the model performance on challenging cases such as the ones presented in Figures 10 and A1. Examples of additional augmentations include die cropping, geometrical transformation, introducing random surface noise, and point dropping.