## 2 Materials and Methods  
Various studies have been conducted in periodonontology to determine whether alveolar bone loss can be detected by artificial intelligence. We performed a systematic search on PubMed, Google Scholar, and Scopus. The following keywords were used: \"periodontitis\" AND (\"CNN\" OR \"Generative Adversarial Network\" OR \"Transformer Network\"OR \"Hybrid Network\") AND \"alveolar bone loss\". A total of 762 studies were initially identified (Pubmed = 78, Google scholar = 513, Scopus = 171). A total of 74 duplicates were identified and removed from the dataset, leaving 688 papers. The titles and abstract of the remaining studies were reviewed to assess their relevance to the topic. Studies that did not focus on AI applications in periodontitis diagnosis and ABL assessment were excluded. Studies that mentioned AI but did not use neural networks were also excluded. At this stage, 666 studies were removed for not being consistent with the review topic. At this point, a full text review of the remaining 22 studies were made, assessing methodological quality, dataset size, AI method used, and relevance to the clinical application in periodontal bone loss detection. To be included in the review, studies had to meet the following criteria: publication date between 2017 and 2024; neural network employed; radiographic images used for detecting ABL or periodontal disease-related bone defects; and AI performance metrics reported. Studies were excluded if they met any of the following conditions: non-AI based approaches; non-relevant clinical application; and lack of sufficient data of performance metrics). After final inclusion, the selected studies were categorized based on the type of neural network architecture used. The process of selecting, skimming, and categorizing papers is represented in Figure 1.  
In selecting the studies, we considered the type of radiographic images used, dataset size, image resolution, type of AI used, assessment of alveolar bone loss, reference test, and availability of code. The most commonly used types of images were OPT (n = 12) followed by periapical endoral radiographs (n = 10); the number of images used in the dataset ranged from 12 to 103,909, the resolution ranged from 128 \(\times\) 128 up to 148 \(\times\) 2276; the AI methods are convolutional neural network (n = 16), hybrid networks (n = 4), transformer network (n = 1), and generative adversarial network (n = 1); alveolar bone loss was assessed by percentage (n = 6), present or absent (n = 6), or stage (mild, moderate, and severe) (n = 7) and CAL measurement (n = 3); different solutions were chosen as reference tests and to create a label to be assigned to the images used in the study. Table 1 presents an overview of these studies. We divided the selected studies based on the type of artificial intelligence used: convolutional neural network; hybrid neural network; generative adversarial network; and transformer network. CNNs, whose structure is shown in Figure 2, are among the oldest and most popular DL models, mainly used for image recognition tasks through supervised learning. Figure 1: Flowchart describing the various stages of research.