## 1 Introduction  
Osteoarthritis (OA) is the most common arthritis occurring at the temporomandibular joint (TMJ). OA is a destructive process in which deformation of the osteoarticular surface of the mandibular condyle and mandibular fossa occurs. The most common cause of OA is excessive mechanical loading on joint tissue. When load is continuously applied and the articular surface softens (chondromalacia), resorption of the subarticular bone appears. A progressive bone change leads to loss of the subchondral cortical layer and bone erosion, which becomes evident in radiological osteoarthritis [1].  
TMJ OA can be diagnosed through medical history, clinical diagnosis, and radiographic examination. TMJ OA clinically shows characteristic signs, including limited movement of the lower jaw due to pain, crepitus, and local paraspinal tenderness in joint promotion. OA can be determined when a radiographic examination shows structural bone change [2]. In addition, OA is useful for evaluating the condylar and ramal asymmetry of the mandible in patients with juvenile idiopathic arthritis (JIA) using orthopantomographies [3].  
Panoramic radiographs are widely used in the early stages of diagnosis when bone change in the TMJ is suspected. However, bone change is difficult to identify by simple imaging because the TMJ has small bone structures at the joint site, and the joint is covered by the wide skull [4]. Furthermore, owing to insufficiently demineralized bone tissue in the early stage of osteoarthritis, structural changes or lesions in the TMJ are often not identified in simple radiographs [5]. Thus, skilled experts with sufficient clinical experience are required for reading panoramic radiographs and additional radiography should be recommended if necessary. Unfortunately, it is inconvenient to transmit panoramic radiographs to a professional reading expert and wait for the result when medical staff who can accurately diagnose osteoarthritis based on panoramic radiographs are unavailable on-site. Moreover, because it is difficult for a doctor to immediately determine the treatment status of osteoarthritis, the task of transmitting panoramic radiographs and waiting for the reading results must, inevitably, be repeatedly performed.  
To resolve these problems, this paper proposes a method for automatically diagnosing osteoarthritis of the TMJ using artificial intelligence (AI) technology. Recent developments in AI technology have provided various methods of analyzing X-ray images [6]. The applicability of convolutional neural network (CNN) technology has been validated in many areas, including extracting specific parts of X-ray images and identifying abnormalities [7; 8; 9; 10; 11; 12]. Many studies have been conducted on dental X-ray image analysis, including tooth detection through panoramic image analysis, osteoporosis analysis, and sinusitis analysis. However, studies using only panoramic X-ray analysis are still insufficient.  
Thus, this study focused on automated detection of the mandibular condyle, including cases where the mandibular condylar region is truncated because of incorrect imaging or the mandibular condylar region is indistinguishable from the surrounding structures owing to the failure to adjust the radiographic density. Recently-developed CNN technology was applied to medical imaging to develop a new algorithm for diagnosing and classifying the severity of OA occurring in the TMJ. The detection accuracy of the newly developed algorithm was analyzed using a validation dataset containing radiological images. Furthermore, the AI model was used to develop an expert system that can be immediately applied in clinical practice sites.