###### Abstract  
Periodontitis is one of the most prevalent diseases worldwide. The degree of radiographic bone loss can be used to assess the course of therapy or the severity of the disease. Since automated bone loss detection has many benefits, our goal was to develop a multi-object detection algorithm based on artificial intelligence that would be able to detect and quantify radiographic bone loss using standard two-dimensional radiographic images in the maxillary posterior region. This study was conducted by combining three recent online databases and validating the results using an external validation dataset from our organization. There were 1414 images for training and testing and 341 for external validation in the final dataset. We applied a Keypoint RCNN with a ResNet-50-FPN backbone network for both boundary box and keypoint detection. The intersection over union (IoU) and the object keypoint similarity (OKS) were used for model evaluation. The evaluation of the boundary box metrics showed a moderate overlapping with the ground truth, revealing an average precision of up to 0.758. The average precision and recall over all five folds were 0.694 and 0.611, respectively. Mean average precision and recall for the keypoint detection were 0.632 and 0.579, respectively. Despite only using a small and heterogeneous set of images for training, our results indicate that the algorithm is able to learn the objects of interest, although without sufficient accuracy due to the limited number of images and a large amount of information available in panoramic radiographs. Considering the widespread availability of panoramic radiographs as well as the increasing use of online databases, the presented model can be further improved in the future to facilitate its implementation in clinics. radiographic bone loss; alveolar bone loss; maxillofacial surgery; deep learning; classification; artificial intelligence; object detection +
Footnote †: journal: Medical  
marginal gingival level [2]. There are, however, differences in methodologies, instruments, applied forces, and techniques that limit the reproducibility of this method [6; 7]. Although the reproducibility of this method can vary between examiners, there are rules that clinicians should/must follow to standardize those values as much as possible. There is also a range of discrepancies established from classic papers in the field of periodontology considering various parameters such as the diameter, the force used, the angle, the positioning of the probe, and the current state of inflammation of the tissue examined [7; 8]. The radiological measurement can be a valuable tool for a more objective diagnosis for planning therapy. Radiological examination of bone loss alterations often include the measurement of the length from the marginal alveolar bone to the tooth apex or the measurements from the cementoenamel junction to the tooth apex [9]. These measurements are characterized by better predictability and a more accurate examination modality in certain cases [2; 10]. However, in the early phase of periodontal disease, it is not a reliable method to detect the first signs or small changes due to the delayed bone reaction and the necessary extent of demineralization that has occurred [2; 10]. Panoramic radiographs are one of the most commonly used imaging techniques in dentistry. Compared with three-dimensional imaging, it offers many advantages such as widespread availability, low radiation exposure, and a good overview of the most important structures in the maxilla and mandible [11; 12]. The use of two-dimensional radiographic images is a common diagnostic tool in the assessment of periodontitis [13], a disease that results in the loss of periodontal attachment, and consequently, visible bone loss. The degree of radiographic bone loss can be used to assess the course of therapy or the severity of the disease [14]. It is necessary to initiate or adjust treatment if the bone loss exceeds a certain level within a specified period of time.