AI model to detect contact relationship between maxillary sinus and posterior teeth

Abstract

Objectives: To establish a novel deep learning networks (MSF-MPTnet) based on panoramic radiographs (PRs) for automatic assessment of relationship between maxillary sinus floor (MSF) and maxillary posterior teeth (MPT), and to compare accuracy of MSF-MPTnet, dentists and radiologists identifying contact relationship. Study design: A total of 1035 PRs and 1035 Cone-beam computed tomographys （CBCT）images were collected from January 2018 to April 2022. The relationships were classified into class I and II by CBCT. Class I represents non-contact group, and class II represents contact group. 350 PRs were randomly selected as test dataset and accuracy of MSF-MPTnet, dentists, and radiologists was compared. Results: The intraclass correlation coefficient of dentists was 0.460–0.690 and it was 0.453–0.664 for radiologists. Sensitivity and accuracy of MSF-MPTnet were 0.682–0.852and 0.890–0.951, indicating that the output performance of MSF-MPTnet was reliable. Accuracy of maxillary premolars and molars were 79.7%–90.3 %, 76.2%–89.2 % and 72.9%–88.3 % in MSF-MPTnet model, dentists and radiologists. Accuracy of class I relationship in the MSF-MPTnet model (67.7%–94.6 %) was higher than that of dentists (56.5%–84.6 %) in maxillary first premolars and right second premolar, and accuracy of class I relationship in the MSF-MPTnet model is also higher than radiologists (40.0%–78.1 %) in all teeth positions (p < 0.05). Conclusions: MSF-MPTnet model could increase detecting accuracy of the relationship between MSF and MPT, minimize pseudo contact relationship and reduce frequency of CBCT use.

Keywords

• Artificial intelligence
• Deep learning/machine learning
• Radiology
• Oral diagnosis
• Decision-making