AI model promising in accurately identifying dental implant systems on low-quality radiographs: Study

Moreover, the accuracy performance of low-quality and distorted DIS classification was compared using AI and five periodontists. Based on a test dataset, the performance evaluation of the AI model achieved accuracy, precision, recall, and F1 score metrics of 95.05%, 95.91%, 92.49%, and 94.17%, respectively. However, five periodontists classified nine types of DISs based on four different low-quality and distorted radiographs, achieving a mean overall accuracy of 37.2 ± 29.0%. Within the limitations of this study, AI demonstrated superior accuracy in identifying DIS from low-quality or distorted radiographs, outperforming dental professionals in classification tasks. However, extensive standardization research on low-quality and distorted radiographic images is essential for actual clinical application of AI.

Reference:

Lee, JH., Kim, YT. & Lee, JB. Identification of dental implant systems from low-quality and distorted dental radiographs using AI trained on a large multi-center dataset. Sci Rep 14, 12606 (2024). https://doi.org/10.1038/s41598-024-63422-z