AI Model Promising in Accurately Identifying Dental Implant Systems

A study published in the Scientific Reports suggests that an AI model is promising in accurately identifying dental implant systems on low-quality radiographs.

Background

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.

Methodology

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 were used as test datasets. These types include:

• Images not perpendicular to the axis of the fixture
• Radiation overexposure
• Cut off the apex of the fixture
• Containing foreign bodies

Results

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 the following metrics:

• Accuracy: 95.05%
• Precision: 95.91%
• Recall: 92.49%
• F1 Score: 94.17%

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%.

Conclusion

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