## 4 Discussion
It is very time-consuming and resource-intensive to manually determine the radiological bone loss, which is why automation such as through artificial intelligence is essential for an accurate and reproducible recording of bone loss progression [46]. When image features are combined with a deep learning model that detects both objects and keypoints, examiner-specific differences can be eliminated, and high reproducibility can be ensured for diagnostics and treatment [33]. Aside from providing objective quality findings, such algorithms can be used to recognize and number the teeth and the region to be examined, thereby saving a great deal of time when completing the treatment card [48]. Although two-dimensional imaging has many advantages, it also has certain limitations. In comparison to single-tooth images, the large field of view reduces the resolution of individual teeth [33]. Although a 3-dimensional body can be represented on a 2-dimensional image, there are certain limitations. [PERSON] et al., for example, were able to demonstrate that roots that protruded into the maxillary sinus in a 2D image did not make contact with it when viewed in 3D. Additionally, teeth may appear to be inclined when they are not. All of these variations are explained by the fact that a volume is represented on a flat plane [49]. A second limitation of artificial intelligence is that its complex algorithms do not represent the decision-making process in an obvious manner, obscuring information that might be useful to practitioners for making their own choices compared to a human-based detection approach [33; 50]. A well-known bias in studies investigating machine learning is the use of the same dataset for both validation and training. Therefore, we applied a five-fold cross-validation and used an independent validation dataset to prevent this error [24; 51; 52]. Machine learning has the disadvantage of requiring a large amount of comparable data to implement its algorithms. In order for artificial intelligence to be able to make a final decision, it is necessary for it to receive very precise instructions on what it should recognize or classify. The annotation of an image is required in order to identify and classify specific areas within it. Artificial intelligence implementation in clinical practice, however, is challenging due to the time-consuming nature of this step. The resolution of problems by algorithms will be impacted by unreliable annotations, inhomogeneous, or too homogeneous (e.g., overfitting) image inputs for the learning process [21]. Our goal has been to generate the most extensive dataset possible for the training process by using several databases in order to take these aspects into consideration. It is worth noting that the training dataset consists of three separate datasets, which were created using different hardware, software, and hospital standards. The model metrics may have been adversely affected by these factors. In spite of this, it has also led to a more generalizable approach compared to a methodology where only one institution is involved. Insufficiently annotated data may lead to worse results in a new validation set if the algorithm learns this error within the training dataset [53]. To validate the model's performance, we used a separate database that was collected within our institute. It is necessary to collect further large datasets in order to be able to overcome individual factorssuch as the different imaging characteristics of scanners or different recording standards and create uniform data. The relationship with clinically implemented scores such as the PSI as well as other periodontal measurements should also be addressed in further clinical studies in a prospective design. It has already been demonstrated by [PERSON] et al. that AI-based algorithms are capable of diagnosing periodontal tissue loss using periapical radiographs [45].