INTEGRATING AI IN THE DIAGNOSIS AND THERAPY OF MAXILLARY BONE DEFICIENCIES - Capatina Andreea; Asaftei Oana; Tibeica Andreea; Agop-Forna Doriana; Shokraei Gholamreza; Norina Forna (Romanian Journal of Medical and Dental Education)
The aim of this study is to explore recent advancements in AI applications in dental diagnostics, focusing on the detection and management of maxillary bone deficiencies using panoramic radiographs and other imaging modalities and providing an overview of the materials and methods employed, presenting the results and discussions from existing research, and drawing conclusive insights. Results The application of AI in these studies has yielded impressive results, indicating that deep learning models can reliably identify periodontal bone loss. These findings underscore the potential of AI to significantly improve the early detection and diagnosis of periodontal disease, thereby enhancing patient outcomes. Conclusions Integrating AI in dental diagnostics not only means automating tasks but also improving their accuracy and reliability. Traditional methods for diagnosing periodontal disease involve both clinical and radiological examinations, which can be subject to observer variability and potential errors. AI systems, trained on large datasets, offer a high degree of consistency and reproducibility. The high intra- and inter-examiner correlation coefficients (ICC) reported in these studies highlight the reliability of AI-assisted diagnostics.