## 4 Discussion
The misdetection of cross-sectional images in the original model from these images was around 60%. However, the total misdetection in all augmented models was 50.61%. This caused us to conclude that adding one type of image for training would not obviously improve object detection performance for that one type of image classification. The result may differ for each type of data augmentation technique used. Adding all kinds of images may improve the overall performance of the object detection model. The authors speculated that the positive predictive value for the 4.2 \(\times\) 12, 5.0 \(\times\) 12, Int SFE, and Lat SFE images for the cross-sectional and panoramic images were approximately 77.78-90.00% and 66.67-100%, respectively. These images were loaded around 47-68 images for each image category, which were quite predictable when compared to the wide range of 0-100% in 4.2 \(\times\) 8, 5.0 \(\times\) 8, 4.2 \(\times\) 10, and 5.0 \(\times\) 10 images that had only 13-23 images. So, it could be predicted that, when the images are packed with at least 50 images, the trained model will respond to at least 70.00%, with regard to positive prediction. Moreover, the balance of data distribution had a great impact on the model's performance. Faster R-CNNs are the preferred solutions for imaging analysis and have been employed in many fields [8; 9; 10; 15; 16]. Some successful applications in which this model has been applied in the field of dentistry are discussed in the paragraphs that follow. A dental-related faster R-CNN model about automatic detection of periodontitis compromised teeth in the panoramic image model reported 81.00% accuracy when 100 digital panoramic images were trained [17]. Likewise, this study reported almost the same 83.33% for 100 panoramic images trained in the original model, which was similar to our findings. Moreover, the study of a faster R-CNN model associated with a rule-based module for tooth detection and numbering system also reported 91.00% accuracy with an enormously large number (800) of periapical radiographs were trained [18]. In our study, 300 images trained in the original model reported only 60.00-70.00% accuracy. However, the closest samples were observed in our data augmented model. The blurred augmented data of 100 images became 1100 images, which resulted in 83.33% accuracy in the panoramic image. Moreover, 150 images of the colored and noise augmented data became 900 images, and the accuracy of the cross-sectional image for both the colored and noise augmented data were 88.89% and 81.81%, respectively. Overall, their data set was about 800 images, which was greater than our 300 original images. The heterogeneity in their data was also higher than that in our data, which probably meant their model was developed from various kinds of images. It may be assumed that the development of the model should be focused on the great amount of original data rather than applied as a data augmentation technique to overcome the identical quantity of the data set. However, the development of the model may benefit from both data augmentation and addition of new images. Nevertheless, these different outcomes may denote that, with different types of images used in the training process, such as periapical radiographs, panoramic images, and cross-sectional images, the individual outcome may not be compared. This study was unique from the input data used in the training process. The input images were obtained from the actual patients. Each DICOM resulted in one image, which made each image have its own characteristic. Besides, the results were also calculated from the testing images created by real humans as the ground truth, which did not even include the training process. Nevertheless, the limitation is the process of JPEG image extraction from the DICOM was still performed manually, which makes it difficult to obtain the same scale between each image. Although there is a digital ruler attached within the GUI, it would be easier and faster for the AI to learn the scale-adjusted image.