## 1 Introduction  
Chronic periodontitis is a leading cause of the loss of teeth and affects about twenty to fifty percent of the global population [1]. Detecting its progression is important to prevent and further lessen the burden of public health. The diagnosis of periodontal conditions is, however, complex and often requires both clinical and radiologic examination [2]. The diagnosis of alveolar bone loss is determined based on the clinical attachment level, and a radiologic assessment is carried out accordingly [3]. While periodontal bone loss is one of the signs of the presence of periodontal disease and the inflammatory condition of adjacent structures [3], radiographic assessment can provide sufficient information about the pattern and extent of bone loss, aiding in the detection and progression of periodontal disease [4; 5; 6]. Nonetheless, similar to clinical assessment, radiographic examination is also subject to interobserver variation (such as between specialists or general practitioners) [7], leading to potential errors in diagnosis that may affect the timing of treatment initiation and the prevention of further disease-related issues in adjacent areas. The emergence of deep learning in medical imaging has led to a growing interest in the use of convolutional neural networks in the field of radiology. This rapidly advancing area of research has captured the attention of many researchers worldwide [8]. Machine learning has been used in medical imaging for decades, and with its growing popularity, computer-aided detection and diagnosis algorithms have advanced alongside computational technologies [8]. In medical imaging, the application of deep learning has expanded beyond organ segmentation to include lesion detection, characterization, and diagnosis. The widespread use of deep learning has demonstrated its effectiveness and reliability in many aspects of medical image analysis [8; 9]. The incorporation of deep learning in dental radiology has enabled prompt diagnosis and treatment planning. Recent research has concentrated on segmenting anatomical structures and detecting dental problems, including caries, periodontal disease, periapical lesions, sinusitis, cystic or tumor lesions, and anatomical anomalies [7; 10; 11; 12; 13; 14; 15]. As an illustration, [PERSON] et al. compared the detection of periodontal bone loss using two imaging modalities: intraoral radiographs and cone-beam computed tomography (CBCT) scans [6]. Additionally, [PERSON] et al. developed an automatic detection system for the horizontal bone loss of alveolar bone utilizing periapical radiographs [16]. Some studies have also used dental panoramic radiographs to evaluate periodontal bone loss [4; 5]. These studies varied not only in their algorithmic methodology but also in the use of radiography. [PERSON] et al. found no statistically significant differences in the detection of bone loss patterns between intraoral radiographs and CBCT scans. Nevertheless, the use of panoramic radiographs is favored since they enable the assessment of the entire dentition with a single radiation dose [6; 15]. Modern panoramic machines are capable of producing high-quality images that eliminate the need for additional intraoral radiographs, minimizing exposure in cases requiring a full mouth series [13]. However, the limited sample sizes used in previous studies indicate that larger training datasets may improve the performance of deep learning systems [8]. The objective of this study is to develop an automated screening program for detecting periodontal bone loss using panoramic radiographs and to evaluate its potential for practical use. The performance of deep learning in detecting periodontal bone loss automatically on panoramic radiographs will be tested using a large dataset. The significance of this present study lies in its potential to transform the detection and diagnosis of periodontal disease.