## 4 Discussion  
This study attempted to develop a novel approach for implant treatment planning by utilizing an available AI platform with a faster R-CNN algorithm. The annotations of the model outcomes were the main focus in this study, since the detected bounding boxes were still not precise. The learning curve of detection rate and accuracy were almost similar in the original model. Since they were still swinging after the 150 data set was trained, their swings were around 10-20%, which were varied between each image. Based on the learning curve patterns of faster R-CNN, it could assume that the larger data sets are needed to see the further trend for the learning curve that can determine the minimum effective data set needed. This study also integrated a data augmentation algorithm to overcome the limitation of data acquisition. The augmented models were improved differently. The detection curve in the blurred augmented model demonstrated the highest detection, as the model was improved by 12.50% for both the panoramic and cross-sectional images in the last training set, with an overall direction that was trending upward. On the other hand, the accuracy curves were totally different. The accuracy curves in the blurred augmented model were reduced around 20% in both the panoramic and cross-sectional images in the last training set. These results implied that the data augmentation algorithm with blurred image transformation made the outline of the interesting area seem widen, which made the images easier for detection, but with those blurry images, it was difficult to classify the corrected annotation. Moreover, the study of image classification in chest radiographs also reported that the Gaussian blurred augmented model showed 78.00-82.00% accuracy, which was not improved when compared to the non-augmented model (81.00-83.00%) [12]. Besides, the study comparing data augmentation strategies for image classification reported that Gaussian distortion was the worst form of augmentation tested, leading to changes in the accuracy of only +0.05% [13]. In contrast, the other three augmented models, including sharpen, color, and noise, were likely to improve, but the detection dropped on the last training set. The results were even worse than the original model. This phenomenon is still unclear, more data set for training may reveal the reason for this detection dropped. However, the accuracy showed some improvement, and their trends were also upward. The colored augmented model demonstrated the best improvement with 40% for the panoramic image and 18.59% for the cross-sectional image. From these results, color modification seemed to be the better alternative data augmentation algorithm. Furthermore, an interesting issue about color transformation outcomes was published in a study of data augmentation with skin lesion analysis. Scenario C (saturation, contrast, brightness, and hue) resulted in better accuracy than scenario B (saturation, contrast, and brightness), followed by scenario A (no augmentation) in the DenseNet-161 and Inception-v4 models [14]. Since accuracy is the most important issue in dental implantology, the error analysis was utilized by the confusion matrix to evaluate false positives and false negatives. When the models were tested with the images that should be detected with the Int SFE, the original model had 28% misdetection. On the other hand, the results of the blurred and sharpened augmented models showed a slightly improved error of 20.00-27.27%. Likewise, the error that occurred in the colored augmented model was greatly improved at 13.33%. Conversely, the result in the noise augmented model appeared different, the error increased at 42.10%. These controversial outcomes could not summarize that data augmentation has minimized the error incidence. As the cut-off point between Int SFE, Lat SFE, and simple implant placement with 4.2 \(\times\) 8 or 5.0 \(\times\) 8 is very close, these infer that the decision-making in the posterior maxillary region is critical.