Automatic recognition of teeth and periodontal bone loss measurement in digital radiographs using deep-learning artificial intelligence

Background/purpose: Artificial Intelligence (AI) can optimize treatment ap-proaches in dental healthcare due to its high level of accuracy and wide range of applications. This study seeks to propose a new deep learning (DL) ensemble model based on deep Convolu-tional Neural Network (CNN) algorithms to predict tooth position, detect shape, detect re-maining interproximal bone level, and detect radiographic bone loss (RBL) using periapical and bitewing radiographs.Materials and methods: 270 patients from January 2015 to December 2020, and all images were deidentified without private information for this study. A total of 8000 periapical radio-graphs with 27,964 teeth were included for our model. AI algorithms utilizing the YOLOv5 model and VIA labeling platform, including VGG-16 and U-Net architecture, were created as a novel ensemble model. Results of AI analysis were compared with clinicians' assessments.Results: DL-trained ensemble model accuracy was approximately 90% for periapical radio-graphs. Accuracy for tooth position detection was 88.8%, tooth shape detection 86.3%, peri-odontal bone level detection 92.61% and radiographic bone loss detection 97.0%. AI models were superior to mean accuracy values from 76% to 78% when detection was performed by den-tists. Conclusion: The proposed DL-trained ensemble model provides a critical cornerstone for radio-graphic detection and a valuable adjunct to periodontal diagnosis. High accuracy and reli-ability indicate model's strong potential to enhance clinical professional performance and build more efficient dental health services.& COPY; 2023 Association for Dental Sciences of the Republic of China. Publishing services by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons. org/licenses/by-nc-nd/4.0/).