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

Most artificial intelligence (AI) studies have attempted to identify dental implant systems (DISs) while excluding low-quality and distorted dental radiographs, limiting their actual clinical use. This study aimed to evaluate the effectiveness of an AI model, trained on a large and multi-centre dataset, in identifying different types of DIS in low-quality and distorted dental radiographs. Based on the fine-tuned pre-trained ResNet-50 algorithm, 156,965 panoramic and periapical radiological images were used as training and validation datasets, and 530 low-quality and distorted images of four types (including those not perpendicular to the axis of the fixture, radiation overexposure, cut off the apex of the fixture, and containing foreign bodies) were used as test datasets. 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

Keywords:

AI model, promising, accurately, identifying, dental implant, systems, low-quality radiographs, study, Lee, JH., Kim, YT. & Lee, JB, Artificial intelligence, Diagnostic imaging, Deep learning, Scientific Reports