Establishing a novel deep learning model for detecting peri-implantitis

Abstract

Background/purpose: The diagnosis of peri-implantitis using periapical radiographs is crucial. Recently, artificial intelligence may apply in radiographic image analysis effectively. The aim of this study was to differentiate the degree of marginal bone loss of an implant, and also to classify the severity of peri-implantitis using a deep learning model. Materials and methods: A dataset of 800 periapical radiographic images were divided into training (n = 600), validation (n = 100), and test (n = 100) datasets with implants used for deep learning. An object detection algorithm (YOLOv7) was used to identify peri-implantitis. The classification performance of this model was evaluated using metrics, including the specificity, precision, recall, and F1 score. Results: Considering the classification performance, the specificity was 100%, precision was 100%, recall was 94.44%, and F1 score was 97.10%. Conclusion: Results of this study suggested that implants can be identified from periapical radiographic images using deep learning-based object detection. This identification system could help dentists and patients suffering from implant problems. However, more images of other implant systems are needed to increase the learning performance to apply this system in clinical practice.

Keywords

• Implant
• Peri-implantitis
• YOLO
• Deep learning
• Convolutional neural network (CNN)