An artificial intelligence study: automatic description of anatomic landmarks on panoramic radiographs in the pediatric population

• Published in: BMC oral health 2023-10, Vol.23 (1), p.1-764, Article 764
• Main Authors: Bağ, İrem, Bilgir, Elif, Bayrakdar, İbrahim Şevki, Baydar, Oğuzhan, Atak, Fatih Mehmet, Çelik, Özer, Orhan, Kaan
• Format: Article
• Language: English
• Subjects: Algorithms; Anatomic landmarks; Artificial intelligence; Datasets; Deep learning; Dentistry; Facial bones; Labeling; Mandible; Mandibular canal; Maxillary sinus; Medical examination; Mouth; Neural networks; Panoramic radiography; Patients; Pediatric dentistry; Pediatrics; Pedodontics; Radiography; Radiography, Medical; Sinuses; Technology application; Teeth
• ISSN: 1472-6831; 1472-6831
• DOI: 10.1186/s12903-023-03532-8

Background Panoramic radiographs, in which anatomic landmarks can be observed, are used to detect cases closely related to pediatric dentistry. The purpose of the study is to investigate the success and reliability of the detection of maxillary and mandibular anatomic structures observed on panoramic radiographs in children using artificial intelligence. Methods A total of 981 mixed images of pediatric patients for 9 different pediatric anatomic landmarks including maxillary sinus, orbita, mandibular canal, mental foramen, foramen mandible, incisura mandible, articular eminence, condylar and coronoid processes were labelled, the training was carried out using 2D convolutional neural networks (CNN) architectures, by giving 500 training epochs and Pytorch-implemented YOLO-v5 models were produced. The success rate of the AI model prediction was tested on a 10% test data set. Results A total of 14,804 labels including maxillary sinus (1922), orbita (1944), mandibular canal (1879), mental foramen (884), foramen mandible (1885), incisura mandible (1922), articular eminence (1645), condylar (1733) and coronoid (990) processes were made. The most successful F1 Scores were obtained from orbita (1), incisura mandible (0.99), maxillary sinus (0.98), and mandibular canal (0.97). The best sensitivity values were obtained from orbita, maxillary sinus, mandibular canal, incisura mandible, and condylar process. The worst sensitivity values were obtained from mental foramen (0.92) and articular eminence (0.92). Conclusions The regular and standardized labelling, the relatively larger areas, and the success of the YOLO-v5 algorithm contributed to obtaining these successful results. Automatic segmentation of these structures will save time for physicians in clinical diagnosis and will increase the visibility of pathologies related to structures and the awareness of physicians. Keywords: Anatomic landmarks, Artificial intelligence, Deep learning, Panoramic radiography, Pediatric dentistry