###### Abstract
Various factors can contribute to bone loss around teeth and implants including prosthetic restorations, infections, or an imbalance of the immune system, leading to proinflammatory reactions [15; 16]. An assessment of severity based on clinical radiograph images requires both radiological and clinical experience as well as a standardized methodology. Radiographic measurements must be accurate, as even a difference of one to two millimeters is considered a significant deviation in bone loss measurements [15; 17]. Cone beam computer tomography (CBCT) enables a more precise assessment of bone morphology via three-dimensional imaging [18]. However, two-dimensional imaging remains the most common imaging modality in clinical practice due to the advantages outlined above. Therefore, improving the assessment of radiographic bone loss via two-dimensional imaging in clinics is necessary. Inexperienced clinicians are more likely to make diagnostic errors and misdiagnoses based on their learning curve. Consequently, a computerized approach with standardized procedures would be highly beneficial [19]. Artificial intelligence is constantly finding new applications in everyday clinical practice [20]. Generally, artificial intelligence is the result of algorithms that are designed to mimic the decision-making processes of humans [21]. Algorithms of this type are often applied to complex and time-consuming tasks such as risk stratification, diagnostics, and object detection [15; 22]. Artificial intelligence has already been found to be capable of recognizing and classifying many structures in dentistry, ranging from teeth and bone to soft tissues [23; 24; 25]. Aside from its use in identifying structures and patterns, it is increasingly being used to assess a prognosis, determine risk, and predict complications [24; 26; 27]. Based on a systematic review by [PERSON] et al. (2021), AI has already been incorporated into many different areas of dentistry [20]. A particular focus was placed on the treatment of dental diseases and the examination of dental implants [20; 28; 29; 30]. It has been shown that computer-aided diagnosis in radiology can assist clinical users in addressing complex problems by providing a valuable second opinion or objective support [31]. Nevertheless, studies investigating the use of deep learning in maxillofacial imaging are still lacking [10]. Several studies have already demonstrated radiographic bone loss (RBL) measurements using dental imaging [32; 33]. However, in some studies, only the site of bone loss was detected, and no automated distance measurements were taken to document the progression of the loss. A rough direction of disease progression has already been determined using percentage data [10]. With the aid of artificial intelligence-based algorithms, it would be possible to monitor the progression of the disease and to perform faster interventions following tooth extraction. In fact, such sophisticated algorithms can even be used for comparing treatment approaches by examining how bone loss changes over time for pairwise comparisons. The algorithms are also expected to save a significant amount of time and effort in the clinical assessment process as well as potentially reduce health care costs in the long run. The use of artificial intelligence-based assessments of panoramic radiographs has been shown to be a promising method for monitoring periodontitis in recent studies [32; 34; 35]. However, the algorithms employed in these studies were intended to classify periodontal bone loss cases and stagings, rather than to provide absolute measurements of bone loss for monitoring over time. As far as we know, no other study has applied AI-based algorithms to estimate the absolute bone loss or heights for purposes of monitoring.